essay on artificial intelligence ai

Artificial Intelligence Essay for Students and Children

500+ words essay on artificial intelligence.

Artificial Intelligence refers to the intelligence of machines. This is in contrast to the natural intelligence of humans and animals. With Artificial Intelligence, machines perform functions such as learning, planning, reasoning and problem-solving. Most noteworthy, Artificial Intelligence is the simulation of human intelligence by machines. It is probably the fastest-growing development in the World of technology and innovation . Furthermore, many experts believe AI could solve major challenges and crisis situations.

Types of Artificial Intelligence

First of all, the categorization of Artificial Intelligence is into four types. Arend Hintze came up with this categorization. The categories are as follows:

Type 1: Reactive machines – These machines can react to situations. A famous example can be Deep Blue, the IBM chess program. Most noteworthy, the chess program won against Garry Kasparov , the popular chess legend. Furthermore, such machines lack memory. These machines certainly cannot use past experiences to inform future ones. It analyses all possible alternatives and chooses the best one.

Type 2: Limited memory – These AI systems are capable of using past experiences to inform future ones. A good example can be self-driving cars. Such cars have decision making systems . The car makes actions like changing lanes. Most noteworthy, these actions come from observations. There is no permanent storage of these observations.

Type 3: Theory of mind – This refers to understand others. Above all, this means to understand that others have their beliefs, intentions, desires, and opinions. However, this type of AI does not exist yet.

Type 4: Self-awareness – This is the highest and most sophisticated level of Artificial Intelligence. Such systems have a sense of self. Furthermore, they have awareness, consciousness, and emotions. Obviously, such type of technology does not yet exist. This technology would certainly be a revolution .

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Applications of Artificial Intelligence

First of all, AI has significant use in healthcare. Companies are trying to develop technologies for quick diagnosis. Artificial Intelligence would efficiently operate on patients without human supervision. Such technological surgeries are already taking place. Another excellent healthcare technology is IBM Watson.

Artificial Intelligence in business would significantly save time and effort. There is an application of robotic automation to human business tasks. Furthermore, Machine learning algorithms help in better serving customers. Chatbots provide immediate response and service to customers.

AI can greatly increase the rate of work in manufacturing. Manufacture of a huge number of products can take place with AI. Furthermore, the entire production process can take place without human intervention. Hence, a lot of time and effort is saved.

Artificial Intelligence has applications in various other fields. These fields can be military , law , video games , government, finance, automotive, audit, art, etc. Hence, it’s clear that AI has a massive amount of different applications.

To sum it up, Artificial Intelligence looks all set to be the future of the World. Experts believe AI would certainly become a part and parcel of human life soon. AI would completely change the way we view our World. With Artificial Intelligence, the future seems intriguing and exciting.

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Artificial Intelligence Essay

500+ words essay on artificial intelligence.

Artificial intelligence (AI) has come into our daily lives through mobile devices and the Internet. Governments and businesses are increasingly making use of AI tools and techniques to solve business problems and improve many business processes, especially online ones. Such developments bring about new realities to social life that may not have been experienced before. This essay on Artificial Intelligence will help students to know the various advantages of using AI and how it has made our lives easier and simpler. Also, in the end, we have described the future scope of AI and the harmful effects of using it. To get a good command of essay writing, students must practise CBSE Essays on different topics.

Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is concerned with getting computers to do tasks that would normally require human intelligence. AI systems are basically software systems (or controllers for robots) that use techniques such as machine learning and deep learning to solve problems in particular domains without hard coding all possibilities (i.e. algorithmic steps) in software. Due to this, AI started showing promising solutions for industry and businesses as well as our daily lives.

Importance and Advantages of Artificial Intelligence

Advances in computing and digital technologies have a direct influence on our lives, businesses and social life. This has influenced our daily routines, such as using mobile devices and active involvement on social media. AI systems are the most influential digital technologies. With AI systems, businesses are able to handle large data sets and provide speedy essential input to operations. Moreover, businesses are able to adapt to constant changes and are becoming more flexible.

By introducing Artificial Intelligence systems into devices, new business processes are opting for the automated process. A new paradigm emerges as a result of such intelligent automation, which now dictates not only how businesses operate but also who does the job. Many manufacturing sites can now operate fully automated with robots and without any human workers. Artificial Intelligence now brings unheard and unexpected innovations to the business world that many organizations will need to integrate to remain competitive and move further to lead the competitors.

Artificial Intelligence shapes our lives and social interactions through technological advancement. There are many AI applications which are specifically developed for providing better services to individuals, such as mobile phones, electronic gadgets, social media platforms etc. We are delegating our activities through intelligent applications, such as personal assistants, intelligent wearable devices and other applications. AI systems that operate household apparatus help us at home with cooking or cleaning.

Future Scope of Artificial Intelligence

In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is becoming a popular field in computer science as it has enhanced humans. Application areas of artificial intelligence are having a huge impact on various fields of life to solve complex problems in various areas such as education, engineering, business, medicine, weather forecasting etc. Many labourers’ work can be done by a single machine. But Artificial Intelligence has another aspect: it can be dangerous for us. If we become completely dependent on machines, then it can ruin our life. We will not be able to do any work by ourselves and get lazy. Another disadvantage is that it cannot give a human-like feeling. So machines should be used only where they are actually required.

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Home — Essay Samples — Information Science and Technology — Modern Technology — Artificial Intelligence

Essays on Artificial Intelligence

Writing an essay on artificial intelligence is not just an academic exercise; it's a chance to explore the cutting-edge innovations and the profound impact AI has on our lives. For students looking to delve deeper into this topic, utilizing the best AI tools for students can provide a significant edge in crafting a well-researched and analytical essay. 🚀 So, get ready to unlock the potential of AI with your words!

Artificial Intelligence Essay Topics for "Artificial Intelligence" 📝

Choosing the right topic is key to writing a compelling essay. Here's how to pick the perfect one:

Artificial Intelligence Argumentative Essay 🤨

Argumentative AI essays require you to take a stance on AI-related issues. Here are ten thought-provoking topics:

• 1. The ethical implications of AI in autonomous weaponry.
• 2. Should AI be granted legal personhood and rights?
• 3. Analyze the impact of AI on the job market and employment prospects.
• 4. The role of AI in addressing climate change and environmental challenges.
• 5. Discuss the risks and benefits of AI in healthcare and medical diagnostics.
• 6. AI's impact on privacy and surveillance in modern society.
• 7. Evaluate the use of AI in education and personalized learning.
• 8. The role of AI in improving cybersecurity and data protection.
• 9. Discuss the potential biases and discrimination in AI algorithms.
• 10. AI and its implications for creativity and the arts.
• 11. The Ethical Implications of Programming Bias into Artificial Intelligence

Artificial Intelligence Cause and Effect Essay 🤯

Dive into cause and effect relationships in the AI realm with these topics:

• 1. Explore how AI-powered virtual assistants have changed communication habits.
• 2. Analyze the effects of AI-driven predictive policing on crime rates.
• 3. Discuss how AI-driven healthcare advancements have extended human lifespans.
• 4. The consequences of AI-powered autonomous vehicles on transportation and traffic safety.
• 5. Investigate the impact of AI algorithms on social media echo chambers and polarization.
• 6. The influence of AI-driven personalized marketing on consumer behavior.
• 7. Explore how AI has revolutionized the entertainment industry and storytelling.
• 8. Analyze the cause and effect of AI's role in financial markets and investment strategies.
• 9. Discuss the effects of AI on reducing energy consumption and sustainable living.
• 10. The consequences of AI in aiding scientific research and discovery.

Artificial Intelligence Opinion Essay 😌

Express your personal views and interpretations on AI through these essay topics:

• 1. Share your opinion on the potential dangers of superintelligent AI.
• 2. Discuss your perspective on AI's role in enhancing human capabilities.
• 3. Express your thoughts on the future of work in an AI-dominated world.
• 4. Debate the significance of AI in addressing global challenges like pandemics.
• 5. Share your views on the ethical responsibilities of AI developers and researchers.
• 6. Discuss the impact of AI on human creativity and innovation.
• 7. Express your opinion on AI's influence on education and personalized learning.
• 8. Debate the ethics of AI in decision-making, such as self-driving car dilemmas.
• 9. Share your perspective on AI's potential to bridge the digital divide and promote equity.
• 10. Discuss your favorite AI-related invention or innovation and its implications.

Artificial Intelligence Informative Essay 🧐

Inform and educate your readers with these informative AI essay topics:

• 1. Explore the history and evolution of artificial intelligence.
• 2. Provide an in-depth analysis of popular AI technologies like deep learning and neural networks.
• 3. Investigate the significance of AI in autonomous robotics and space exploration.
• 4. Analyze the role of AI in natural language processing and language translation.
• 5. Examine the applications of AI in climate modeling and environmental conservation.
• 6. Explore the cultural and societal impacts of AI in science fiction literature and films.
• 7. Provide insights into the ethics of AI in medical decision-making and diagnosis.
• 8. Analyze the potential for AI in disaster response and emergency management.
• 9. Discuss the role of AI in enhancing cybersecurity and threat detection.
• 10. Examine the future trends and possibilities of AI in various industries.
• 11. Ethical Implications of AI in Healthcare: Patient Privacy
• 12. Impact of AI on Government Services: Study of Role in UPSC Exam Process

Artificial Intelligence Essay Example 📄

Artificial intelligence thesis statement examples 📜.

Here are five examples of strong thesis statements for your AI essay:

• 1. "The rapid advancements in artificial intelligence present both unprecedented opportunities and ethical dilemmas, as we navigate the journey toward an AI-driven future."
• 2. "In analyzing the impact of AI on healthcare, we unveil a transformative force that promises to revolutionize medical diagnosis and treatment, but also raises concerns about data privacy and security."
• 3. "The development of superintelligent AI systems demands careful consideration of ethical frameworks to ensure their responsible and beneficial integration into society."
• 4. "Artificial intelligence is not a replacement for human creativity but a powerful tool that amplifies our capabilities, ushering in an era of unprecedented innovation and discovery."
• 5. "AI-driven autonomous vehicles represent a technological leap that holds the potential to reshape transportation, reduce accidents, and increase accessibility, but also raises questions about liability and safety."

Artificial Intelligence Essay Introduction Examples 🚀

Here are three captivating introduction paragraphs to begin your essay:

• 1. "In a world driven by data and algorithms, artificial intelligence has emerged as both a beacon of innovation and a source of profound ethical contemplation. As we embark on this essay journey into the realm of AI, we peel back the layers of silicon and software to explore the implications, promises, and challenges of our AI-driven future."
• 2. "Imagine a world where machines not only assist us but also think, learn, and adapt. The rise of artificial intelligence has ignited a conversation that transcends technology—it delves into the very essence of human potential and the responsibilities we bear as creators. Join us as we navigate the AI landscape, one algorithm at a time."
• 3. "In an era marked by digital transformations and the ubiquity of smart devices, artificial intelligence stands as the sentinel of change. As we step into the world of AI analysis, we are confronted with a paradox: the immense power of machines and the ethical dilemmas they pose. Together, let's dissect the AI phenomenon, from its inception to its potential to shape the destiny of humanity."

Artificial Intelligence Conclusion Examples 🌟

Conclude your essay with impact using these examples:

• 1. "As we draw the curtains on this AI exploration, we stand at the intersection of innovation and ethics. Artificial intelligence, with all its wonders and complexities, challenges us to not only harness its power for progress but also to ensure its responsible and ethical use. The journey continues, and the conversation evolves as we navigate the evolving landscape of AI."
• 2. "In the closing frame of our AI analysis, we reflect on the ever-expanding possibilities and responsibilities that AI brings to our doorstep. The pages of this essay mark a beginning—a call to action. Together, we have explored the AI landscape, and the future is now in our hands, waiting for our choices to shape it."
• 3. "As the AI narrative reaches its conclusion, we find ourselves at the crossroads of human ingenuity and artificial intelligence. The journey has been both enlightening and thought-provoking, reminding us that the future of AI is a collaborative endeavor, guided by ethics, curiosity, and a shared vision of a better world."

Application of Ai in Transportation and Renewable Energy

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Ethical Issues in Using Ai Technology Today

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Artificial intelligence (AI) refers to the intellectual capabilities exhibited by machines, contrasting with the innate intelligence observed in living beings, such as animals and humans.

The inception of artificial intelligence research as an academic field can be traced back to its establishment in 1956. It was during the renowned Dartmouth conference of the same year that artificial intelligence acquired its distinctive name, definitive purpose, initial accomplishments, and notable pioneers, thereby earning its reputation as the birthplace of AI. The esteemed figures of Marvin Minsky and John McCarthy are widely recognized as the founding fathers of this discipline.

Early pioneers such as John McCarthy, Marvin Minsky, and Allen Newell played instrumental roles in shaping the foundations of AI research. In the following years after its original inception, AI witnessed both periods of optimism and periods of skepticism, as researchers explored different approaches and techniques. Notable breakthroughs include the development of expert systems in the 1970s, which aimed to replicate human knowledge and reasoning, and the emergence of machine learning algorithms in the 1980s and 1990s. The turn of the 21st century witnessed significant advancements in AI, with the rise of big data, powerful computing technologies, and deep learning algorithms. This led to remarkable achievements in areas such as natural language processing, computer vision, and autonomous systems.

There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness.

Healthcare: AI assists in medical diagnosis, drug discovery, personalized treatment plans, and analyzing medical images. Finance: AI is used for automated trading, fraud detection, risk assessment, and customer service through chatbots. Transportation: AI powers autonomous vehicles, traffic optimization, logistics, and supply chain management. Entertainment: AI contributes to recommendation systems, AI-generated music and art, virtual reality experiences, and content creation. Cybersecurity: AI helps in detecting and preventing cyber threats and enhancing network security. Agriculture: AI optimizes farming practices, crop management, and precision agriculture. Education: AI enables personalized learning, adaptive assessments, and intelligent tutoring systems. Natural Language Processing: AI facilitates language translation, voice assistants, chatbots, and sentiment analysis. Robotics: AI powers robots in various applications, such as manufacturing, healthcare, and exploration. Environmental Conservation: AI aids in environmental monitoring, wildlife protection, and climate modeling.

John McCarthy: Coined the term "artificial intelligence" and organized the Dartmouth Conference in 1956, which is considered the birth of AI as an academic discipline. Marvin Minsky: A cognitive scientist and AI pioneer, Minsky co-founded the Massachusetts Institute of Technology's AI Laboratory and made notable contributions to robotics and cognitive psychology. Geoffrey Hinton: Renowned for his work on neural networks and deep learning, Hinton's research has greatly advanced the field of AI and revolutionized areas such as image and speech recognition. Andrew Ng: An influential figure in the field of AI, Ng co-founded Google Brain, led the development of the deep learning framework TensorFlow, and has made significant contributions to machine learning algorithms. Fei-Fei Li: A prominent researcher in computer vision and AI, Li has made groundbreaking contributions to image recognition and has been a strong advocate for responsible and ethical AI development.. Demis Hassabis: Co-founder of DeepMind, a leading AI research company, Hassabis has made notable contributions to areas such as deep reinforcement learning and has led the development of groundbreaking AI systems. Elon Musk: Although primarily known for his role in space exploration and electric vehicles, Musk has also made notable contributions to AI through his involvement in companies like OpenAI and Neuralink, advocating for AI safety and ethics.

1. According to a report by IDC, global spending on AI systems is expected to reach $98.4 billion in 2023, indicating a significant increase from the $37.5 billion spent in 2019. 2. The job market for AI professionals is thriving. LinkedIn's 2021 Emerging Jobs Report listed AI specialist as one of the top emerging jobs, with a 74% annual growth rate over the past four years. 3. AI-powered chatbots are revolutionizing customer service. A study by Oracle found that 80% of businesses plan to use chatbots by 2022. Furthermore, 58% of consumers have already interacted with chatbots for customer support, indicating the growing acceptance and adoption of AI in enhancing customer experiences. 4. McKinsey Global Institute estimates that by 2030, automation and AI technologies could contribute to a global economic impact of $13 trillion. 5. The healthcare industry is leveraging AI for improved patient care. A study published in the journal Nature Medicine reported that an AI model was able to detect breast cancer with an accuracy of 94.5%, outperforming human radiologists.

The topic of artificial intelligence (AI) holds immense importance in today's world, making it an intriguing subject to explore in an essay. AI has revolutionized multiple facets of human life, ranging from technology and business to healthcare and transportation. Understanding its significance is crucial for comprehending the potential and impact of this rapidly evolving field. Firstly, AI has the power to reshape industries and transform economies. It enables automation, streamlines processes, and enhances efficiency, leading to increased productivity and economic growth. Moreover, AI advancements have the potential to address complex societal challenges, such as healthcare accessibility, environmental sustainability, and resource management. Secondly, AI raises ethical considerations and socio-economic implications. Discussions on privacy, bias, job displacement, and AI's role in decision-making become essential for navigating its responsible implementation. Examining the ethical dimensions of AI fosters critical thinking and encourages the development of guidelines and regulations to ensure its ethical use. Lastly, exploring AI allows us to envision the future possibilities and risks associated with this technology. It sparks discussions on the boundaries of machine intelligence, the potential for sentient AI, and the impact on human existence. By studying AI, we gain insights into technological progress, its limitations, and the responsibilities associated with harnessing its potential.

1. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. 3. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking. 4. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. 5. Chollet, F. (2017). Deep Learning with Python. Manning Publications. 6. Domingos, P. (2018). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. 7. Ng, A. (2017). Machine Learning Yearning. deeplearning.ai. 8. Marcus, G. (2018). Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage. 9. Winfield, A. (2018). Robotics: A Very Short Introduction. Oxford University Press. 10. Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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Artificial Intelligence - Essay Samples And Topic Ideas For Free

A college essay on AI allows one to delve into the intriguing world where machines and algorithms shape the future. It demonstrates how exciting the field of advanced technology and its impact on human society can be. When preparing a persuasive and argumentative essay on artificial intelligence, it is essential to explore the potential danger and benefits associated with AI.

To begin, select from a range of compelling essay topics related to this. This could include exploring the ethical implications of AI, the role of it in healthcare, or its impact on the job market. Conduct careful analysis using reputable sources. For example, it can be an interesting research paper on artificial intelligence or free samples to support your arguments.

The next step is to formulate a clear and concise thesis statement that will convey your position on the topic. Creating an outline will help you with this. Each paragraph in the body should focus on a specific aspect of AI, such as cybernetic systems or the ethical considerations surrounding AI development.

In the introduction of your paper, you can highlight the rapid advancements in AI and its pervasive presence in various industries. It will be useful to mention such an organization as OpenAI. Do not forget to craft a captivating hook to capture the reader’s attention. It could be a thought-provoking question, a startling statistic, or a compelling anecdote. No matter what you choose, it should emphasize the significance of AI technology in today’s world. At the conclusion of the essay, all you have to do is summarize the key points discussed in your paper.

Should Humanity Fear Advances in Artificial Intelligence

Nowadays, there are a lot of talks and debates on Artificial Intelligence (AI) and its future. This is an issue which is increasingly causing concern amongst a significant portion of the world's population. But before discussing fear of advances in AI, first, it is better to clearly know what AI is. "AI can be seen as a collection of technologies that can be used to imitate or even to outperform tasks performed by humans using machines" (Bollegala, 2016, para. 4). […]

Benefits of Artificial Intelligence

Artificial intelligence is the theory and development of computer systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision making and translation between languages. Artificial intelligence has its advantages and disadvantages. Some of these advantages would be the few mistakes they would make; some of these robots could be used to explore the space that goes to the moon or other planets, also to explore the deepest oceans and mining. One of the […]

Negative Effects of Social Media

Social media is a vast platform, luring us in with a lot of different content. The amount of interaction one can have with people online within the span of a day is surreal. So, it becomes self-evident that platforms that have so much impact on our lives should be truly understood, and this research will seek to educate people on the negative impact of social media on society. So why is social media bad? To say good doesn’t exist without […]

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Use of Artificial Intelligence in Medicine

In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. For example, music, toys and games, transportation, finance, hospitals and medicine, news, publishing & writing, aviation, and heavy industries. Not only that, but "it has increased the level of performance of physicians at hospital facilities. The situation acts in the interest of patients who are regarded as customers" (Nadimpalli, 1). According to the […]

Why Artificial Intelligence a Serious Problem

Technology is in our lives every day. Smartphones, computers, tablets, and laptops have all become extensions of ourselves. Now, a new type of technology has appeared: artificial intelligence. Unlike previous technology, artificial intelligence is just that. A machine that simulates intelligence. It does this so well that nobody can tell the difference. Artificial intelligence, or AI, is divided into three subsections: artificial narrow intelligence, artificial general intelligence, and artificial superintelligence (Pasichnyk and Strelkova). Artificial narrow intelligence (ANI) is AI specifically […]

How AI is Beneficial to Society

Artificial intelligence may be the last invention humans will ever need to make. AI is the development of a computer system able to perform a task that normally requires human intelligence. People tend to disagree about-about the evolvement of AI because they will soon become faster and more capable than humans. AI is beneficial to society because they help with enforcing the laws and solving crimes, military use, and ethical issues. In particular, AI 's are beneficial when it comes […]

Use of Artificial Intelligence in Marketing

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Revolutions are Seen as Positive Advancements

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The Beauty and Danger of Artificial Intelligence

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Why Artificial Intelligence Must be Regulated

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The Connection of Artificial Intelligence and Marketing

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How To Write An Essay On Artificial Intelligence

Introduction to the concept of artificial intelligence.

When writing an essay on artificial intelligence (AI), it's important to start by defining what AI is and its significance in the modern world. Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. The introduction should provide a brief overview of the development of AI, from its inception to its current state. This will set the stage for a deeper exploration of various aspects of AI, such as its applications, ethical considerations, and potential future developments. Your introduction should also clearly state your thesis or main argument, which will guide the direction of your essay.

Exploring the Applications and Benefits of AI

The body of your essay should delve into the various applications and benefits of AI in different sectors. Discuss how AI is transforming industries such as healthcare, finance, transportation, and more. For instance, in healthcare, AI can assist in diagnosing diseases and personalizing treatment plans. In finance, AI algorithms are used for risk assessment and fraud detection. Highlight the efficiency, accuracy, and cost-effectiveness AI brings to these fields. This part of the essay should provide concrete examples of AI applications, demonstrating the significant impact of AI on improving various aspects of society and business.

Addressing Ethical and Societal Implications

An essential aspect of writing about AI is addressing the ethical and societal implications. Discuss the ethical dilemmas posed by AI, such as privacy concerns, job displacement due to automation, and the potential misuse of AI technologies. Explore how AI could affect social dynamics, including the digital divide and biases in AI algorithms. This section should also consider how regulations and policies are being developed to guide the ethical development and deployment of AI. The objective here is to present a balanced view that not only highlights the advancements AI brings but also critically examines the challenges and concerns it poses.

Concluding with Future Perspectives on AI

Conclude your essay by summarizing the main points discussed and offering a perspective on the future of AI. Reflect on the potential advancements in AI technology and what they could mean for society. Consider the role of AI in shaping future job markets, its integration in everyday life, and how it might evolve in the coming years. Discuss the importance of responsible innovation and the role of governments, industries, and academia in shaping the future of AI. A well-crafted conclusion will not only bring closure to your essay but also encourage further thought and discussion about the role of AI in shaping our future.

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How artificial intelligence is transforming the world

Subscribe to techstream, darrell m. west and darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies john r. allen john r. allen.

April 24, 2018

Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making—and already it is transforming every walk of life. In this report, Darrell West and John Allen discuss AI’s application across a variety of sectors, address issues in its development, and offer recommendations for getting the most out of AI while still protecting important human values.

Table of Contents I. Qualities of artificial intelligence II. Applications in diverse sectors III. Policy, regulatory, and ethical issues IV. Recommendations V. Conclusion

• 49 min read

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. 1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values. 2

In order to maximize AI benefits, we recommend nine steps for going forward:

• Encourage greater data access for researchers without compromising users’ personal privacy,
• invest more government funding in unclassified AI research,
• promote new models of digital education and AI workforce development so employees have the skills needed in the 21 st -century economy,
• create a federal AI advisory committee to make policy recommendations,
• engage with state and local officials so they enact effective policies,
• regulate broad AI principles rather than specific algorithms,
• take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,
• maintain mechanisms for human oversight and control, and
• penalize malicious AI behavior and promote cybersecurity.

Qualities of artificial intelligence

Although there is no uniformly agreed upon definition, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.” 3  According to researchers Shubhendu and Vijay, these software systems “make decisions which normally require [a] human level of expertise” and help people anticipate problems or deal with issues as they come up. 4 As such, they operate in an intentional, intelligent, and adaptive manner.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics. 5 Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved “experience” is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

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Applications in diverse sectors

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways. 6

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030.” 7 That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that “AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption.” 8 Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion. 9 According to observers in that sector, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.” 10 In addition, there are so-called robo-advisers that “create personalized investment portfolios, obviating the need for stockbrokers and financial advisers.” 11 These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decisionmaking. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions. 12 Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. 13 That dramatically increases storage capacity and decreases processing times.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels. 14

National security

AI plays a substantial role in national defense. Through its Project Maven, the American military is deploying AI “to sift through the massive troves of data and video captured by surveillance and then alert human analysts of patterns or when there is abnormal or suspicious activity.” 15 According to Deputy Secretary of Defense Patrick Shanahan, the goal of emerging technologies in this area is “to meet our warfighters’ needs and to increase [the] speed and agility [of] technology development and procurement.” 16

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time—if not eventually in real time—thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position “humans in the loop” in a hyperwar scenario will ultimately dictate the West’s capacity to be competitive in this new form of conflict. 17

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a “thinking” defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating “WannaCry” and “Petya” viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China’s State Council issued a plan for the country to “build a domestic industry worth almost $150 billion” by 2030. 18 As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs. 19 The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well. 20

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that “detects lymph nodes in the human body in Computer Tomography (CT) images.” 21 According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they “predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital.” 22

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven “Strategic Subject List” that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity. 23

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates. 24

However, critics worry that AI algorithms represent “a secret system to punish citizens for crimes they haven’t yet committed. The risk scores have been used numerous times to guide large-scale roundups.” 25 The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Despite these concerns, other countries are moving ahead with rapid deployment in this area. In China, for example, companies already have “considerable resources and access to voices, faces and other biometric data in vast quantities, which would help them develop their technologies.” 26 New technologies make it possible to match images and voices with other types of information, and to use AI on these combined data sets to improve law enforcement and national security. Through its “Sharp Eyes” program, Chinese law enforcement is matching video images, social media activity, online purchases, travel records, and personal identity into a “police cloud.” This integrated database enables authorities to keep track of criminals, potential law-breakers, and terrorists. 27 Put differently, China has become the world’s leading AI-powered surveillance state.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector. 28

Autonomous vehicles—cars, trucks, buses, and drone delivery systems—use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps. 29

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key—not the physical car or truck itself.

Since these cameras and sensors compile a huge amount of information and need to process it instantly to avoid the car in the next lane, autonomous vehicles require high-performance computing, advanced algorithms, and deep learning systems to adapt to new scenarios. This means that software is the key, not the physical car or truck itself. 30 Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. 31

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services—such as Uber and Lyft in the United States, Daimler’s Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China—demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service. 32

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred. 33 Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call—whether a patient can be treated on-site or needs to be taken to the hospital—by taking into account several factors, such as the type of call, location, weather, and similar calls. 34

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.

Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a “City Hall To Go” that makes sure underserved communities receive needed public services. It also has deployed “cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots.” San Francisco has certified 203 buildings as meeting LEED sustainability standards. 35

Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology. Among the top applications noted in the report are “smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement.” 36

Policy, regulatory, and ethical issues

These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times.

At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices? What about questions of legal liability in cases where algorithms cause harm? 37

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a “data-friendly ecosystem with unified standards and cross-platform sharing.” AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are “accessible for exploration” in the research community is a prerequisite for successful AI development. 38

According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U.S. ranks eighth overall in the world, compared to 93 for China. 39

But right now, the United States does not have a coherent national data strategy. There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers.

Biases in data and algorithms

In some instances, certain AI systems are thought to have enabled discriminatory or biased practices. 40 For example, Airbnb has been accused of having homeowners on its platform who discriminate against racial minorities. A research project undertaken by the Harvard Business School found that “Airbnb users with distinctly African American names were roughly 16 percent less likely to be accepted as guests than those with distinctly white names.” 41

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person’s face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, “If your facial recognition data contains mostly Caucasian faces, that’s what your program will learn to recognize.” 42 Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody’s risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated—what school our children go to and what opportunities they have. We don’t have to bring the structural inequalities of the past into the future we create. 43

AI ethics and transparency

Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made. 44

In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans–based Bricolage Academy “gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school’s broad geographic area.” 45 Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don’t like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. 46

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have “the right to opt out of personally tailored ads” and “can contest ‘legal or similarly significant’ decisions made by algorithms and appeal for human intervention” in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the “black box” operates. 47

Legal liability

There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation’s facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms. 48 The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.

In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm “requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service.” By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. 49 But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

Recommendations

In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity. 50 Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. 51 As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.

Google long has made available search results in aggregated form for researchers and the general public. Through its “Trends” site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy. 52 That helps people track movements in public interest and identify topics that galvanize the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events.

In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients.

There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning.

Some combination of these approaches would improve data access for researchers, the government, and the business community, without impinging on personal privacy. As noted by Ian Buck, the vice president of NVIDIA, “Data is the fuel that drives the AI engine. The federal government has access to vast sources of information. Opening access to that data will help us get insights that will transform the U.S. economy.” 53 Through its Data.gov portal, the federal government already has put over 230,000 data sets into the public domain, and this has propelled innovation and aided improvements in AI and data analytic technologies. 54 The private sector also needs to facilitate research data access so that society can achieve the full benefits of artificial intelligence.

Increase government investment in AI

According to Greg Brockman, the co-founder of OpenAI, the U.S. federal government invests only $1.1 billion in non-classified AI technology. 55 That is far lower than the amount being spent by China or other leading nations in this area of research. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics. Higher investment is likely to pay for itself many times over in economic and social benefits. 56

Promote digital education and workforce development

As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.

For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. 57 The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be “big picture” thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM’s Teacher Advisor program, utilizing Watson’s free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. 58 As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.

In order to move forward in this area, several members of Congress have introduced the “Future of Artificial Intelligence Act,” a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a “climate of investment and innovation to ensure the global competitiveness of the United States,” “optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce,” “support the unbiased development and application of artificial intelligence,” and “protect the privacy rights of individuals.” 59

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration 540 days after enactment regarding any legislative or administrative action needed on AI.

This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

States and localities also are taking action on AI. For example, the New York City Council unanimously passed a bill that directed the mayor to form a taskforce that would “monitor the fairness and validity of algorithms used by municipal agencies.” 60 The city employs algorithms to “determine if a lower bail will be assigned to an indigent defendant, where firehouses are established, student placement for public schools, assessing teacher performance, identifying Medicaid fraud and determine where crime will happen next.” 61

According to the legislation’s developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won’t go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. 62 It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The European Union has taken a restrictive stance on these issues of data collection and analysis. 63 It has rules limiting the ability of companies from collecting data on road conditions and mapping street views. Because many of these countries worry that people’s personal information in unencrypted Wi-Fi networks are swept up in overall data collection, the EU has fined technology firms, demanded copies of data, and placed limits on the material collected. 64 This has made it more difficult for technology companies operating there to develop the high-definition maps required for autonomous vehicles.

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, “Regulations prohibit any automated decision that ‘significantly affects’ EU citizens. This includes techniques that evaluates a person’s ‘performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.’” 65 In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the “black boxes” and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, “The key problem confronting predictive analytics is really transparency. We’re in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing.” 66

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning “cyberbullying, stock manipulation or terrorist threats,” as well as “entrap[ping] people into committing crimes.” Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, “An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.” 67 His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for “nondeception” and “honesty,” according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness. 68

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a “voting-based system” that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account. 69 This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

As with any emerging technology, it is important to discourage malicious treatment designed to trick software or use it for undesirable ends. 70 This is especially important given the dual-use aspects of AI, where the same tool can be used for beneficial or malicious purposes. The malevolent use of AI exposes individuals and organizations to unnecessary risks and undermines the virtues of the emerging technology. This includes behaviors such as hacking, manipulating algorithms, compromising privacy and confidentiality, or stealing identities. Efforts to hijack AI in order to solicit confidential information should be seriously penalized as a way to deter such actions. 71

In a rapidly changing world with many entities having advanced computing capabilities, there needs to be serious attention devoted to cybersecurity. Countries have to be careful to safeguard their own systems and keep other nations from damaging their security. 72 According to the U.S. Department of Homeland Security, a major American bank receives around 11 million calls a week at its service center. In order to protect its telephony from denial of service attacks, it uses a “machine learning-based policy engine [that] blocks more than 120,000 calls per month based on voice firewall policies including harassing callers, robocalls and potential fraudulent calls.” 73 This represents a way in which machine learning can help defend technology systems from malevolent attacks.

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. 74 Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.

Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by Amazon. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. 

John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.

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Artificial Intelligence

Governance Studies

Center for Technology Innovation

Artificial Intelligence and Emerging Technology Initiative

Julian Jacobs, Francesco Tasin, AJ Mannan

April 25, 2024

Elaine Kamarck, Darrell M. West

August 27, 2024

Jolynn Dellinger, Stephanie K. Pell

April 18, 2024

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Comprehensive argumentative essay paper on artificial intelligence, rachel r.n..

• February 22, 2024

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Unraveling the Promise and Peril of Artificial Intelligence

Artificial Intelligence (AI) stands as a hallmark of human innovation, promising to revolutionize industries, economies, and even the fabric of society itself. With its ability to mimic cognitive functions, AI has penetrated various spheres of human existence, from healthcare to finance, transportation to entertainment. However, this technological marvel is not without its controversies and ethical dilemmas. This essay delves into the multifaceted landscape of artificial intelligence, exploring its potential, challenges, and implications for humanity.(Comprehensive Argumentative Essay Paper on Artificial Intelligence)

AI holds the promise of unlocking unprecedented levels of efficiency and productivity across industries . In healthcare, AI-driven diagnostic tools can analyze vast amounts of medical data to detect diseases with higher accuracy and speed than human physicians. Moreover, AI-powered robotic surgeries enable minimally invasive procedures, reducing patient recovery times and risks. In manufacturing, AI-driven automation streamlines production processes, leading to cost savings and higher output. Self-driving cars, a pinnacle of AI innovation, promise safer roads and greater mobility for individuals, while also potentially reducing traffic congestion and emissions.(Comprehensive Argumentative Essay Paper on Artificial Intelligence)

Furthermore, AI has revolutionized the way we interact with technology, enhancing user experiences through natural language processing and personalized recommendations. Virtual assistants like Siri and Alexa have become ubiquitous, simplifying tasks and providing timely information at our fingertips. AI-driven recommendation algorithms power platforms like Netflix and Spotify, catering to individual preferences and shaping our consumption habits.(Comprehensive Argumentative Essay Paper on Artificial Intelligence)

Despite its transformative potential, AI also raises significant concerns regarding privacy , security, and the displacement of human labor. The proliferation of AI-powered surveillance systems raises alarms about encroachments on personal privacy and civil liberties. Facial recognition technology, for instance, poses risks of mass surveillance and wrongful identifications. Moreover, the reliance on AI for critical decision-making, such as in criminal justice or financial markets, raises questions about accountability and transparency. Biases embedded in AI algorithms can perpetuate social inequalities and discrimination, amplifying existing societal injustices.(Comprehensive Argumentative Essay Paper on Artificial Intelligence)

Furthermore, the widespread adoption of AI-driven automation threatens to disrupt labor markets, leading to job displacement and widening economic disparities. Low-skilled workers are particularly vulnerable to being replaced by AI-powered systems, exacerbating socio-economic inequalities. Moreover, the concentration of AI capabilities in the hands of a few powerful corporations raises concerns about monopolistic practices and the concentration of wealth and power.(Comprehensive Argumentative Essay Paper on Artificial Intelligence)

The ethical implications of AI extend beyond its practical applications to f undamental questions about the nature of intelligence, consciousness, and autonomy. As AI systems become increasingly sophisticated, they blur the lines between machine and human cognition, raising questions about the moral status of AI entities. Should AI systems be granted rights and responsibilities akin to human beings? Can AI possess consciousness and subjective experiences? These philosophical inquiries challenge our understanding of personhood and moral agency in the age of artificial intelligence.(Comprehensive Argumentative Essay Paper on Artificial Intelligence)

Furthermore, the development and deployment of AI raise profound ethical dilemmas regarding accountability and control. Who should be held responsible when AI systems malfunction or make erroneous decisions with significant consequences? How can we ensure that AI aligns with human values and ethical principles? These questions underscore the importance of ethical frameworks and regulatory mechanisms to govern the development and use of AI technology responsibly.(Comprehensive Argumentative Essay Paper on Artificial Intelligence)

In conclusion, artificial intelligence holds immense promise as a transformative force for human society, offering solutions to complex problems and augmenting human capabilities. However, its rapid advancement also poses significant challenges and ethical dilemmas that demand careful consideration. As we navigate the evolving landscape of AI, it is imperative to strike a balance between innovation and responsibility, ensuring that AI serves the collective good while upholding fundamental human values and rights. Only through thoughtful reflection, ethical deliberation, and inclusive governance can we harness the full potential of artificial intelligence for the betterment of humanity.(Comprehensive Argumentative Essay Paper on Artificial Intelligence)

Owe, A., & Baum, S. D. (2021). Moral consideration of nonhumans in the ethics of artificial intelligence.  AI and Ethics ,  1 (4), 517-528. https://scholar.google.com/citations?user=lJxa2TEAAAAJ&hl=en&oi=sra

Heinrichs, B. (2022). Discrimination in the age of artificial intelligence.  AI & society , 1-12. https://link.springer.com/article/10.1007/s00146-021-01192-2

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• Artificial Intelligence Essay

Essay on Artificial Intelligence

Artificial Intelligence is the intelligence possessed by the machines under which they can perform various functions with human help. With the help of A.I, machines will be able to learn, solve problems, plan things, think, etc. Artificial Intelligence, for example, is the simulation of human intelligence by machines. In the field of technology, Artificial Intelligence is evolving rapidly day by day and it is believed that in the near future, artificial intelligence is going to change human life very drastically and will most probably end all the crises of the world by sorting out the major problems. 

Our life in this modern age depends largely on computers. It is almost impossible to think about life without computers. We need computers in everything that we use in our daily lives. So it becomes very important to make computers intelligent so that our lives become easy. Artificial Intelligence is the theory and development of computers, which imitates the human intelligence and senses, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has brought a revolution in the world of technology. 

Artificial Intelligence Applications

AI is widely used in the field of healthcare. Companies are attempting to develop technologies that will allow for rapid diagnosis. Artificial Intelligence would be able to operate on patients without the need for human oversight. Surgical procedures based on technology are already being performed.

Artificial Intelligence would save a lot of our time. The use of robots would decrease human labour. For example, in industries robots are used which have saved a lot of human effort and time. 

In the field of education, AI has the potential to be very effective. It can bring innovative ways of teaching students with the help of which students will be able to learn the concepts better. 

Artificial intelligence is the future of innovative technology as we can use it in many fields. For example, it can be used in the Military sector, Industrial sector, Automobiles, etc. In the coming years, we will be able to see more applications of AI as this technology is evolving day by day. 

Marketing: Artificial Intelligence provides a deep knowledge of consumers and potential clients to the marketers by enabling them to deliver information at the right time. Through AI solutions, the marketers can refine their campaigns and strategies.

Agriculture: AI technology can be used to detect diseases in plants, pests, and poor plant nutrition. With the help of AI, farmers can analyze the weather conditions, temperature, water usage, and condition of the soil.

Banking: Fraudulent activities can be detected through AI solutions. AI bots, digital payment advisers can create a high quality of service.

Health Care: Artificial Intelligence can surpass human cognition in the analysis, diagnosis, and complication of complicated medical data.

History of Artificial Intelligence

Artificial Intelligence may seem to be a new technology but if we do a bit of research, we will find that it has roots deep in the past. In Greek Mythology, it is said that the concepts of AI were used. 

The model of Artificial neurons was first brought forward in 1943 by Warren McCulloch and Walter Pits. After seven years, in 1950, a research paper related to AI was published by Alan Turing which was titled 'Computer Machinery and Intelligence. The term Artificial Intelligence was first coined in 1956 by John McCarthy, who is known as the father of Artificial Intelligence. 

To conclude, we can say that Artificial Intelligence will be the future of the world. As per the experts, we won't be able to separate ourselves from this technology as it would become an integral part of our lives shortly. AI would change the way we live in this world. This technology would prove to be revolutionary because it will change our lives for good. 

Branches of Artificial Intelligence:

Knowledge Engineering

Machines Learning

Natural Language Processing

Types of Artificial Intelligence

Artificial Intelligence is categorized in two types based on capabilities and functionalities. 

Artificial Intelligence Type-1

Artificial intelligence type-2.

Narrow AI (weak AI): This is designed to perform a specific task with intelligence. It is termed as weak AI because it cannot perform beyond its limitations. It is trained to do a specific task. Some examples of Narrow AI are facial recognition (Siri in Apple phones), speech, and image recognition. IBM’s Watson supercomputer, self-driving cars, playing chess, and solving equations are also some of the examples of weak AI.

General AI (AGI or strong AI): This system can perform nearly every cognitive task as efficiently as humans can do. The main characteristic of general AI is to make a system that can think like a human on its own. This is a long-term goal of many researchers to create such machines.

Super AI: Super AI is a type of intelligence of systems in which machines can surpass human intelligence and can perform any cognitive task better than humans. The main features of strong AI would be the ability to think, reason, solve puzzles, make judgments, plan and communicate on its own. The creation of strong AI might be the biggest revolution in human history.

Reactive Machines: These machines are the basic types of AI. Such AI systems focus only on current situations and react as per the best possible action. They do not store memories for future actions. IBM’s deep blue system and Google’s Alpha go are the examples of reactive machines.

Limited Memory: These machines can store data or past memories for a short period of time. Examples are self-driving cars. They can store information to navigate the road, speed, and distance of nearby cars.

Theory of Mind: These systems understand emotions, beliefs, and requirements like humans. These kinds of machines are still not invented and it’s a long-term goal for the researchers to create one. 

Self-Awareness: Self-awareness AI is the future of artificial intelligence. These machines can outsmart the humans. If these machines are invented then it can bring a revolution in human society. 

Artificial Intelligence will bring a huge revolution in the history of mankind. Human civilization will flourish by amplifying human intelligence with artificial intelligence, as long as we manage to keep the technology beneficial.

FAQs on Artificial Intelligence Essay

1. What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science that emphasizes the development of intelligent machines that would think and work like humans.

2. How is Artificial Intelligence Categorised?

Artificial Intelligence is categorized in two types based on capabilities and functionalities. Based on capabilities, AI includes Narrow AI (weak AI), General AI, and super AI. Based on functionalities, AI includes Relative Machines, limited memory, theory of mind, self-awareness.

3. How Does AI Help in Marketing?

AI helps marketers to strategize their marketing campaigns and keep data of their prospective clients and consumers.

4. Give an Example of a Relative Machine?

IBM’s deep blue system and Google’s Alpha go are examples of reactive machines.

5. How can Artificial Intelligence help us?

Artificial Intelligence can help us in many ways. It is already helping us in some cases. For example, if we think about the robots used in a factory, they all run on the principle of Artificial Intelligence. In the automobile sector, some vehicles have been invented that don't need any humans to drive them, they are self-driving. The search engines these days are also AI-powered. There are many other uses of Artificial Intelligence as well.

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• Artificial Intelligence and Its Impact on Education Increased awareness of the benefits of AI in the education sector and the integration of high-performance computing systems in administrative work have accelerated the pace of transformation in the field.
• Artificial Intelligence, Its Benefits & Risks One of the most fascinating things about artificial intelligence is that virtually all artificial intelligence assistants respond in feminine voices. Artificial intelligence is expected to feature in the automobile industry since many companies are looking […] We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
• Why Artificial Intelligence Will Not Replace Human in Near Future? In that case, the hazards of applying AI in areas related to human well-being necessitate a great deal of attention and algorithm transparency.
• The Problem of Artificial Intelligence The introduction of new approaches to work and rest triggered the reconsideration of traditional values and promoted the growth of a certain style of life characterized by the mass use of innovations and their integration […]
• Robots and Artificial Intelligence One the one hand, with artificial intelligence and fully autonomous robots, organizations will be able to optimize their spending and increase the speed of development and production of their commodities.
• Artificial Intelligence: Positive or Negative Innovation? He argues that while humans will still be in charge of a few aspects of life in the near future, their control will be reduced due to the development of artificial intelligence.
• Artificial Intelligence: The Helper or the Threat? To conclude, artificial intelligence development is a problem that leaves nobody indifferent as it is closely associated with the future of the humanity.
• Pros and Cons of Artificial Intelligence I consider Hurley a prominent representative of the opinion that artificial intelligence is not able to change a person and has many weaknesses.
• Ethics and Artificial Intelligence The various AI systems utilize patients’ data without their consent and expose their private information. Many AI systems in the healthcare environment utilize patients’ data and information without their full consent.
• The Age of Artificial Intelligence (AI) The film “In the Age of AI” exhibits the importance of AI in transforming society. According to the documentary, AI integration in the transport sector has made it easier and safer for people to move […]
• Information Technology and Artificial Intelligence The first limitation is the speed of information transfer, which, thanks to the advances in information technology, is becoming faster and faster. Advances in information technology and the AI would have to remove the biological […]
• Artificial Intelligence Transforming the World The possible effects of any program on the community can then be planned for and measured by managers. To conclude, even though most people are unfamiliar with AI, the world is on the verge of […]
• Artificial Intelligence for the Future of Policing To conclude, the implementation of artificial intelligence along with surveillance technologies will help policing maintain control over a big population. Artificial Intelligence allows policing to effectively prevent potential criminal events via the prediction of a […]
• The Effect and Impact of Artificial Intelligence on Consumer Behavior The success of this development incited Turing to publish the article ‘Computing Machinery and Intelligence’ that explained how to create and test intelligent machines. The marketing industry provides a clear insight into the effects and […]
• Impact of Artificial Intelligence on Business Management What is the impact of AI integration among businesses on the employees’ motivation and activities? The primary aim of this research is to gain an in-depth understanding of the impact of AI integration among various […]
• Evie.ai: Artificial Intelligence and Future Work In addition, some definitions and examples of AI for business are given together with discussing the development of tech companies around the globe. Therefore, the global demand for AI is expected to continue increasing in […]
• The Battle of AI – Wajbah Discussion The battle of AI- Wajbah was no doubt a defining moment in the founding of the country Qatar and signaled the end of the Ottoman’s reign over the country.
• Effects of AI on the Accounting Profession They aimed to find out how AI affects the performance of accounting professionals, investigate whether there have there been changes in employee attitudes toward AI, explore factors that could influence changes in the attitudes of […]
• Artificial Intelligence in Healthcare In addition, the improved AI tools will assist in choosing the best method of treatment and predict the likely results of specific solutions.
• Artificial Intelligence and Humans Co-Existence Some strategies to address these challenges exist; however, the strict maintenance of key areas under human control is the only valid solution to ensure people’s safety.
• Artificial Intelligence in the Military The current paper will provide research on the virtues, shortcomings, and perspectives of the use of AI in the military. The issue of the usage of AI in military actions is highly controversial and has […]
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• Artificial Intelligence in Healthcare and Medicine As a result of this review, a better understanding of the current state of artificial intelligence in healthcare settings will be acquired, additionally, the review will function as the analysis for the quality of the […]
• Artificial Intelligence as Technological Advancement Automation and digitization of healthcare services, marketing, and financial systems through AI are beneficial. While AI is significant for social and economic growth, its application in education should be limited.
• Artificial Intelligence in the Transportation Industry Following that, key achievements in the transportation business included the introduction of bicycles in the early nineteenth century, automobiles in the 1890s, railroads in the nineteenth century, and airplanes in the twentieth century.
• Artificial Intelligence and Copyright Laws The United States Copyright Office has refused to give rights to a person whose camera has been used by a monkey to take a selfie, after which the country’s copyright practices compendium has been expanded […]
• Artificial Intelligence in Self Driving Cars The field of Artificial intelligence is one of the newest areas in science and engineering. When explained in terms of thinking critically, AI is the desired outcome of human effort to make computers think, portrayed […]
• Evie.ai: Artificial Intelligence and Future Workforce That is why it is crucial to analyze some trends in artificial intelligence that are being adopted by many businesses for better functioning, in particular, the implementation of Evie.ai, its functions, and the consequences.
• Evie.ai Company in Artificial Intelligence Market The application of artificial intelligence to manufacturing and agriculture is gradually expanding from the commercial and service industries. The global demand for AI is high, and it is expected to grow exponentially.
• Application of Artificial Intelligence in Business The connection of AI and the business strategy of an organization is displayed through the ability to use its algorithm for achieving competitive advantage and maintaining it.
• Artificial Intelligence Advantages and Disadvantages In the early years of the field, AI scientists sort to fully duplicate the human capacities of thought and language on the digital computer.
• AI and Job Security Aspects The biggest fear in the economy’s digitalization is the loss of jobs, but this will only be in the short term and only if there is the uncontrolled use of artificial intelligence.
• Artificial Intelligence: Application and Future The programmable digital computer, a device built on the abstract core of mathematical reasoning, was created due to this work in the 1940s.
• Using of AI in Supply Chain Management Second, it is essential to select the right provider of an AI system that will apply to these goals. The AI-based system is a quick and precise way to achieve SCM objectives.
• Artificial Intelligence in Marketing Artificial intelligence in marketing is a method of using customer data and AI concepts, including machine learning, to predict the next step of the consumer and meet his needs, even those that the consumer has […]
• Jobs & Technology: “In the Age of AI” Documentary The documentary film In the Age of AI by Frontline provides answers to these questions and talks about the prospect of retaining jobs and professions, the development of technology, rivalry between China and the United […]
• Cutting-Edge Technologies: Blockchain and AI The users can sign in their accounts and transact to another user and the blockchain records the transaction which is viewed by other users. It is difficult to collect taxes and trace malicious activities in […]
• Artificial Intelligence in “I, Robot” by Alex Proyas To begin with, AI is defined by Nilsson as a field of computer science that attempts to enhance the level of intelligence of computer systems.
• Artificial Intelligence in the Documentary “Transcendent Man” The artificial intelligence is becoming a threat to the existence of humanity since these machines are slowly but steadily replacing the roles of mankind in all spheres of life.
• Artificial Intelligence: Pros and Cons Artificial intelligence, or robots, one of the most scandalous and brilliant inventions of the XX century, causing people’s concern for the world safety, has become one of the leading branches of the modern science, which […]
• How Artificial Intelligence Affects the Stock Market End-to-end machine learning under the umbrella of AI has given a chance to have quality and quantity data science that can be used in analysis during stock trading.
• Harris’s “Can We Build AI…?” Talk: Rhetorical Analysis These statements are examples of the use of logos since they are logically intuitive to the point that any member of the general audience can understand.
• The Artificial Intelligence Use in Solar Panels The use of solar PV panels as sources of renewable energy has been gaining traction in the recent decades. This implies that the output of energy in PV solar panels is often unstable.
• Artificial Intelligence Technology for Nursing However, the Internet may also provide misleading or factually inaccurate data, and it may be difficult to detect useful information in the pile of non-reliable data.
• Artificial Intelligence and Related Social Threats It may be expressed in a variety of ways, from peaceful attempts to attract attention to the issue to violent and criminal activities.
• Artificial Intelligence Managing Human Life Although the above examples explain how humans can use AI to perform a wide range of tasks, it is necessary for stakeholders to control and manage the replication of human intelligence.
• Artificial Intelligence and Future of Sales It is assumed that one of the major factors that currently affect and will be affecting sales in the future is the artificial intelligence.
• Artificial Intelligence for Recruitment and Selection As a result, the hiring process has changed considerably, and it is crucial to comprehend how social media and technology affect it. In conclusion, technology and social networking have had a big impact on both […]
• Artificial Intelligence and Gamification in Hiring It is evident that the connection between the current scientific controversy and the work by Mary Shelley is expressed in a conflict between science and morality in the context of the desire to penetrate the […]
• Artificial Intelligence and Frankenstein’s Monster: Article Review These models learn from the world around them and might eventually become sentient, and it is far from certain that they will decide to be benevolent to humans.
• The Aspects of the Artificial Intelligence The goal of artificial intelligence, a subfield of computer science and engineering, is to build intelligent machines that can think and learn similarly to people.
• Benefits and Repercussions of Artificial Intelligence The main advantages of AI implementation are higher precision of performed work and more free time for humans, while the possible repercussions are an increase in the unemployment rate and malicious use of private data.
• Artificial Intelligence: Exhibiting Goal-Oriented Behavior Most people may accept and adapt to AI monitoring their health and shopping habits in the upcoming years. It will give people the tools to adapt to a constantly changing and complicated world without stress.
• Using AI to Diagnose and Treat Depression One of the main features of AI is the ability to machine learning, that is, to use data from past experiences to learn and modify algorithms in the future.
• Artificial Intelligence Bot for Depression By increasing the availability and accessibility of mental health services, these technologies may also contribute to the development of cognitive science practices in Malaysia.
• Artificial Intelligence as an Agent of Change This trend is planned to increase, and by 2024 the global use of AI in the energy industry will reach $7.
• Artificial Intelligence in Soil Health Monitoring Therefore, the new value provided by AI technology is that it allows automation and algorithm-based predictions for more solid decision-making. AI in soil health monitoring is an unconventional application of the technology, albeit capable of […]
• Insurance Companies Using Artificial Intelligence In these situations, the decisions AI would make would not contribute to improving the situation for these people and would not better the society as a whole.
• Artificial Intelligence’s Impact on Communication Therefore, it is worth concluding that although artificial intelligence is now at a high level of development, the communication of technologies among themselves needs to be improved. The bots will be trained by artificial intelligence […]
• Propositional and First-Order Logic in Artificial Intelligence Artificial intelligence’s propositional logic analyzes sentences as variables, and in the event of complicated sentences, the first phase is to deconstruct the sentence into its component variables.
• Ethical Considerations of AI Becoming Sentient However, the more monotonous and routine a task is, the more likely it is that AI can provide meaningful assistance and even discover ways to perfect tasks by identifying patterns that can be adapted to […]
• Artificial Intelligence Algorithms and Methods to Use One of the strengths of using one algorithm over another is that it can be more easily adapted to fit the needs of different problems.
• Artificial Intelligence: Bridging the Gap to Human-Level Intelligence Numerous functions characterize human intelligence and some of them are implemented in artificial intelligence systems, but the main difference between human and artificial intelligence is the ability to synthesize new knowledge and identify unknown patterns.
• Artificial Intelligence as a Tool in Healthcare To begin with, AI is an efficient technology that can be implemented in healthcare to increase the productivity of employees. To elaborate, the ability of this technology to convert data into knowledge allows AI to […]
• Artificial Intelligence in Healthcare Administration The key stakeholders in addressing healthcare inefficiencies in the administrative processes include the government, hospital administrators and the direct-patient contact staff.
• How AI and Machine Learning Influence Marketing in the Fashion Industry As governments shut down factories, stores, and events to stop the transmission of the virus, the COVID-19 pandemic has had a tremendous impact on the worldwide fashion industry.
• The Utilization of Artificial Intelligence In addition, the introduction of artificial intelligence has a negative effect on reducing the level of work ethic and enthusiasm. In conclusion, the most significant limitations of artificial intelligence are rising unemployment and increasing laziness.
• Artificial Intelligence and Machine Learning There are both benefits and challenges to the use of AI and ML in the customer complaint resolution process. The ability of a company to provide a customer experience depends on that business’s power to […]
• Artificial Intelligence: Past, Present, and Future Quantum-based artificial intelligence and its use in the military, including intelligence gathering and interpretation, present an interesting field of research. This paper addresses the use of artificial intelligence systems based on quantum technologies in the […]
• Artificial Intelligence and Legal Codes The degree of success of the banking business depends to a large extent on the ability of the institution to maintain confidentiality.
• Would Artificial Intelligence Reduce the Shortage of the Radiologists As founders of the digital world in healthcare, Radiologists may now welcome AI as a new partner in their profession, along with the possibility for radiology to play a more significant role in healthcare, as […]
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• “Artificial Intelligence in Healthcare” and “From Spreading to Embedding Innovation” That is, the narration in the article is free of ill-founded value judgments, and the language corresponds to the article’s subject matter, which is artificial intelligence in healthcare.
• Master of Artificial Intelligence At a certain point in the process of inventing and introducing various technological devices intended for the means of mastering and subjugating the surrounding space, for means of communication and calculating his actions, a person […]
• Artificial Intelligence and Machine Learning in Clinical Trials At the same time, to draw contrasts on the application of AI and ML in the health sector, the limitations of the technologies will also be elucidated to highlight areas of improvement that could be […]
• Amazon’s AI-Powered Home Robots The objective of the present plan is to provide a comprehensive analysis and evaluation of the introduction of AI-powered home robots as Amazon’s next disruptive customer product.
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• Will AI Replace Marketing Jobs in the Future? Marketing is among those; the key to success in it lies in constant awareness of the recent tendencies in the market as well as in consumer behavior, which calls for never-ending data analysis.
• Artificial Intelligence and Building Information Modeling Software Tools The second article is by Zhang et al.and it analyzes the interoperability of BIM software tools and addresses the problems in the process of data exchange.
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• Artificial Intelligence and Artificial Life The author of The Algorithms for Love, Liu Ken, writes that humans are too young and too immature to understand the global laws of the universe.
• Implementation of Artificial Intelligence in Healthcare Settings The drivers for the innovation are the increase in the aging population, the National Health Service’s strategies to enhance the well-being of citizens and healthcare services’ quality, and the expansion of modern technologies in other […]
• Gulf Information Technology Exhibition Global X AI 2021 The program, which first debuted in 1981, highlights the transformative ideas that will shape the future of society and commerce in the coming decade. The Future Blockchain Summit was the region’s first and biggest Blockchain […]
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Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

• James Shaw 1 , 13 ,
• Joseph Ali 2 , 3 ,
• Caesar A. Atuire 4 , 5 ,
• Phaik Yeong Cheah 6 ,
• Armando Guio Español 7 ,
• Judy Wawira Gichoya 8 ,
• Adrienne Hunt 9 ,
• Daudi Jjingo 10 ,
• Katherine Littler 9 ,
• Daniela Paolotti 11 &
• Effy Vayena 12  

BMC Medical Ethics volume  25 , Article number:  46 ( 2024 ) Cite this article

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

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Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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Introduction

Principles of the “high road”, the return of theory x using artificial intelligence, the pluses and minuses of ai in the workplace, managing the transition: why the “wrong” choices are made, policy responses to the ai-related and other technological challenges.

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Artificial intelligence in human resource management: a challenge for the human-centred agenda?

(no footnote loaded)

Peter Cappelli

Nikolai Rogovsky

The ILO human-centred agenda puts the needs, aspirations and rights of all people at the heart of economic, social and environmental policies. At the enterprise level, this approach calls for broader employee representation and involvement that could be powerful factors for productivity growth. However, the implementation of the human-centred agenda at the workplace level may be challenged by the use of artificial intelligence (AI) in various areas of corporate human resource management (HRM). While firms are enthusiastically embracing AI and digital technology in a number of HRM areas, their understanding of how such innovations affect the workforce often lags behind or is not viewed as a priority. This paper offers guidance as to when and where the use of AI in HRM should be encouraged, and where it is likely to cause more problems than it solves.

Sustainable development is at the core of national and international discussions on development issues. At the enterprise level, the ILO defines sustainability as “operating a business so as to grow and earn profit, and recognition of the economic and social aspirations of people inside and outside the organization on whom the enterprise depends, as well as the impact on the natural environment” (ILO 2007). According to the ILO, “sustainable enterprises should innovate, adopt environmentally friendly technologies, develop skills and human resources, and enhance productivity to remain competitive in national and international markets” (ILO 2007).

The ILO Centenary Declaration for the Future of Work emphasizes “the role of sustainable enterprises as generators of employment and promoters of innovation and decent work” and, in this regard, underlines the importance of “supporting the role of the private sector as a principal source of economic growth and job creation by promoting an enabling environment for entrepreneurship and sustainable enterprises […] in order to generate decent work, productive employment and improved living standards for all”. Creating “productive workplaces” and “productive and healthy conditions” of work are critical in achieving this goal (ILO 2019a).

At both the macro- and micro-levels, the ILO promotes the “high road” approach to productivity which “seeks to enhance productivity through better working conditions and the full respect for labour rights as compared to the “low road” which consists of the exploitation of the workforce” (ILO, n.d.). The “high road” is related to the ILO’s “human-centred agenda,” which is a key part of the ILO human-centred approach to the future of work highlighted in the ILO Centenary Declaration for the Future of Work and described in-depth in the related Work for a brighter future – Global Commission on the Future of Work report. This approach puts “workers’ rights and the needs, aspirations and rights of all people at the heart of economic, social and environmental policies” (ILO 2019a) and calls for investments in people’s capabilities, institutions of work and in decent and sustainable work (ILO 2019b). It is expected that such investments would be combined with people-centred approach to business practices at the workplace level.

This paper is aimed at exploring when and how AI is used in HRM, and when its impact on firm and individual performance is positive, negative or cannot be properly accessed. We start by looking at the principles of high road approach and how these principles are related to the use of AI in HRM. Then we specifically look at the pluses of minuses of AI in the workplace focusing on such aspects of HRM as hiring and work organization. We conclude with a brief overview of some possible policy responses to the AI-related and other technological challenges.

Since the Western Electric studies that were carried out in the 1920s and 1930s (Landsberger 1958), evidence has accumulated year-by-year about the advantages of taking employee management seriously: look after employees, and they will look after the employer’s interests; empower employees to make decisions, from quality circles to lean production to agile management, and performance and quality improves.

In the 1950s and early 1960s, Douglas McGregor described the developing literature on the effectiveness of management practices as “Theory Y” and contrasted it with “Theory X” which essentially views employees as simply another factor of production like raw materials in manufacturing (McGregor 1960). Frederick Taylor and his scientific management approach were arguably the originators of a sophisticated view of Theory X, which is rooted in a simple, conservative (with a small “c”) notion that employees are mainly motivated by money, need to be told what to do by experts, and will shirk their responsibilities if not watched closely. Theory Y has the much more complex but more accurate assumption that employees have many complicated motivations and if managed correctly would do the right thing for the employer even if they are not monitored or incentivized by financial rewards and punishments. The contemporary incarnation of Theory X and Y with a few new twists is the idea of a “high road” approach for Theory Y practices and a “low road” for Theory X.

In recent decades, evidence has accumulated about the advantages of Theory Y approach of taking employee management seriously and the most fundamental element of that approach, reciprocity: if employers look after the interests of their employees, then the employees in turn will be inclined to look after the interests of their employer.

The ILO data from the Better Work and Sustaining Competitive and Responsible Enterprises (SCORE) programmes 1 provides evidence of the positive effects of such an approach, showing that “improved workplace cooperation, effective workers’ representation, quality management, clean production, human resource management and occupational safety and health, as well as supervisory skills training, particularly among female supervisors, all increase productivity”. Moreover, “better management also helps to lower accidents at work 2 and employee turnover and reduces the occurrence of unbalanced production lines (where work piles up on one line while other workers are sitting idle)”. Evidence also points to “increased productivity and profitability associated with a reduction in verbal abuse and sexual harassment.” 3

Evidence has even moved past showing reductions in turnover and improvements in individual and organizational productivity to financial performance. The strongest of these studies is arguably Edmans (2011) which finds that companies making the “best places to work” ranking have higher than anticipated share prices in future years. A different study finds a similar market-beating performance for companies that have greater managerial integrity and ethics (Guiso, Sapienza and Zingales 2015). Another global study shows that companies that have better management (including more sophisticated human resource practices) perform better on a wide range of economic dimensions (Bloom and Van Reenen 2010).

None of this is to suggest that tracking employee performance, setting standards for their work efforts, and rewarding and punishing are irrelevant. However, relying solely on those tactics is not enough.

At the same time, it is important to note that at least in the short term the “low road” approach to management can allow firms to break-even or even improve economic performance (but not social outcomes) where the initial practices are simplistic. In those countries and sectors where labour standards and laws are not always respected and workers are often not organized and represented, the “low road” approach to productivity is still common, in part because it is simpler for management and may appeal to their world view that focuses on their roles. However, the “low road” approach is seeing something of a resurgence even in the most sophisticated sectors of the world leading economies as we note below.

The use of artificial intelligence (AI) in HRM can challenge the implementation of the ILO-led human-centred agenda at the workplace level. While firms are enthusiastically embracing artificial intelligence and digital technology in a number of their HRM areas, their understanding of how such innovations affect the workforce is often not viewed as a priority or lags behind (Rogovsky and Cooke 2021).

Many enterprises in both developing and developed countries are replacing the employee empowerment approach, such as quality circles and lean production, with an “optimization” approach where experts and the algorithms associated with artificial intelligence (AI) they create take back the decision-making that empowerment had created. Optimization seems to appeal to many managers as it sounds per se to be more efficient. As a result, the evidence of employee empowerment as a productivity driver is largely ignored (Cappelli 2020).

The application of data science as well as an increase in computer power in worker-related questions have spawned a huge number of applications, indeed an entire industry of vendors, offering solutions to virtually every human resource question. It takes the decision-making out of the hands of employees and their supervisors as well, turning it over to the software and ultimately the vendors and their programmers who generate answers to human resource problems. In 2020, 28 per cent of US employers report that they were using data science tools to “replace line manager duties in assigning tasks and managing performance.” 39 per cent were planning to start doing so the following year (Mercer 2020).

The use of AI in the form of data science in workforce management is not per se a bad thing. As with AI in other contexts, it may allow us to answer questions that have not been addressed before: not every AI solution is taking decisions away from humans. For example, advice to employees about possible career paths can be generated for them by machine-learning algorithms based on what has been best in the past for other workers like them. Rigorous advice on questions like this has simply not been available before. It is also the case that decisions currently made by managers and supervisors are often so poor, driven by subjectivity and bias, which makes it easier for data science solutions to do better. In hiring, for example, it is easier for data-based algorithms to do a better job than line managers who have no relevant training and base their decisions largely on subjective opinion. More generally, the lag in productivity growth across most industrialized countries has been caused, at least in part, because not enough investment was made in solutions where “capital,” which includes software, takes over tasks from workers and perform them at less cost. Consider, for example, what it would cost for a large employer that receives thousands of job applications every year if it had to do the initial classification of applications by hand instead of by applicant tracking software.

The issue in terms of guidance is knowing when the application of these AI techniques is useful (i.e. they solve new problems and handle tasks better than humans do) and where they are counterproductive (i.e. they offer no advantage over human decisions and may actually make employment relationships worse).

Finding such a mix is a challenge that involves managerial as well as moral dimensions. At the very least, we believe that when there is a choice between options that are equal in terms of organizational outcomes, employers should choose the one that is better for employees. This principle coincides with standard utilitarian views of ethics and with economic interpretations of Pareto improvements. 4 Perhaps more importantly, it draws on the legal principle in civil law of “abuse of right”, which means that simply because one party has the legal right to do something does not create the right to do it if by doing so it damages other parties without creating benefits (Mughal, unpublished).

There are still very few studies that examine the implications of artificial intelligence for corporate HRM. Tambe, Cappelli and Yakubovich (2019) noted “a substantial gap between the promise and reality of artificial intelligence” in the area of HRM. They identified four major challenges in using artificial intelligence as part of HRM:

complexity of HR phenomena, which make it difficult to model;

limitations of small data sets;

accountability issues associated with fairness and other ethical and legal constraints when decisions are made by algorithms; and

potentially negative employee reactions to managerial decisions taken based on data-based algorithms.

In particular, from both economic and social points of view there is a growing concern over the use of artificial intelligence algorithms for hiring (Cappelli 2019) and for work organization (Cappelli 2020). These issues will be considered next.

It may be easiest to grasp the general principles behind the use of AI through some common examples. Before we look into the “optimization” policies and practices per se , let us focus on hiring which is perhaps the most basic, time-consuming, and important of the employee management questions. The evidence increasingly points to the fact that we do not handle this process well even without AI: we rely on ad hoc methods of finding recruits, mainly just hoping that the right ones come to us, and then we hope that hiring managers, typically untrained in the process who rely on off-the-cuff interviews, will somehow find the best candidates to hire. Then we do not check to see whether the ones we have hired are good or bad so we do not learn from the process. What we do know is that this process gives ample room for biases to influence decisions: my personal views on what constitutes a good cultural “fit” shape who gets hired as does how much I like candidates, which is strongly correlated with how similar they are to me.

Hiring is actually a context where the prospects for algorithms are best. The way data science ideally works starts with machine learning, where the software (the “machine” in this case) looks at the attributes of as many current and past employees as possibly to see how their attributes relate to their quality as employees. The software is agnostic as to what should matter and how it should matter: relationships could be non-linear, simultaneous, in any form. It generates a single equation to measure the attributes that are associated with a good performer, not as with prior “best practice” approaches where there is one score for say IQ, one for prior experience, one for interviews, and so forth. The machine learning algorithm looks at any potential candidate and tells you how similar they are to those in the past who were your best performing employees.

The plus of this approach is that it is objective. Unlike human assessors, it will not give higher scores to more attractive applicants or those most similar to us. Algorithms have the advantage of treating all similar observations the same way: if it is counting a college degree a certain way, it does not give extra credit to the college where the boss is an alumnus. Cowgill (2020) finds that an algorithm used to predict who should advance to short-list status did a better job than human recruiters did in part because it did not over-value credentials that had higher social status such as degrees from elite universities. 5 An algorithm will also find factors that predict that humans with our more limited experience would never find. Another plus is that once set up, using algorithms to hire is remarkably cheaper than relying on humans.

The downside that is common to human assessors is that if prior experience was shaped by bias, then the algorithm will be as well. Amazon’s hiring algorithm, for example, gave higher scores to men because in the past Amazon managers had given higher scores to male employees (Cappelli 2019). Another downside is the issue now known as “explainability”: can we explain to the candidates why they were not hired when they ask why their scores were low? It is difficult for machine learning algorithms to address those questions. Complaints from gig workers that the algorithms managing them are biased have led organizations like the UK-based Workers Info Exchange to press those gig companies to explain to their contractors why and how their algorithms made the decisions they did (Murgia 2021). It also takes very large data sets to generate machine learning algorithms, and few employers hire enough employees to build their own. They are likely as a result to rely on the algorithms produced by vendors with no guarantee or even reason to believe that the vendor’s algorithm will predict hiring success for their jobs.

A related issue is that some of the factors that have been used in generating these algorithms might give us qualms. For example, the commuting distance from one’s home to a job has been shown to be a good predictor of turnover and some aspects of performance. Where one lives, therefore, shapes the likelihood of getting a job. Social media postings are sometimes used in building hiring algorithms as well. Most employers would probably want limits placed on the kind of information on which the algorithms are based, something that is not possible when one uses algorithms produced elsewhere.

From the human-centred point of view, these practices are not only potentially discriminatory as Amazon case shows, but they also prevent decent candidates getting the jobs they deserve.

If hiring is amongst the most promising uses of AI, perhaps the most troublesome is the use of software to determine workers’ schedules. This is not a new idea, but its use has expanded considerably to a wide range of jobs. 6 42 per cent of US companies now use it (Harris and Gurchensky 2020). The goal is a sensible one, to “optimize” work scheduling process in order to minimize total amount of labor needed to cover assignments and make sure that everyone is doing roughly the same amount of work allocated across similar schedules. The reason this approach is troublesome, though, is because we have other approaches that work even better where the employees themselves work out schedules through a process of negotiations and social exchange: I’ll cover for you this weekend if you take my shift next week, for example. Scheduling algorithms cut both employees and supervisors out of the process and end up being quite rigid and unable to respond to last-minute adjustments. 7 A study of optimization approaches in scheduling discovered that it increased turnover and turnover costs while adding nothing to performance outcomes (Kesavan and Kuhnen 2017). The effort to cut costs in one category (headcount) increased them in another (turnover).

The evidence that the flexible approach works is, by the standards of rigorous research, about as good as it gets. It improves a range of outcomes for employees, such as better job attitudes (Baltes et al. 1999), as well as better accommodation of life challenges outside of work including evidence that it is worth extra salary to employees (Kelly et al. 2008). For employers, it leads to higher productivity 8 . Software, in contrast, assumes that the workers are interchangeable, it imposes schedules without any consideration as to the varying needs of individual employees, and it is not at all flexible when last-minute problems pop up. As with many of these new practices, the question is, what problem is it really solving, and is the solution worse than the original problem?

Then we have situations where existing practices that involve empowering employees have worked extremely well yet there is a push to replace them with software. Beginning in the 1970s, efforts to involve employees in solving workplace problems borrowed from Japan by North American and West European companies worked so well that they spread systematically throughout industrialized countries and beyond, starting union-based cooperative programmes on safety problems, to quality circles where workers identified the causes of quality problems, and then to lean production where workers took over some of the tasks of the industrial engineers, redesigning their own jobs to improve productivity and quality. The evidence that lean production in the form of Toyota’s operating model worked so much better than anything else, especially the efforts at GM and Volkswagen to deal with productivity and quality problems with automation, was so clear that it was impossible to ignore (MacDuffie and Pil 1997). Lean production spread from there to other industries including healthcare.

Recently, though, we have seen efforts to replace the employee involvement that was at the heart of lean production with machine learning software. The new approach is called “machine vision.” Rather than having employees figure out what is wrong with their work processes, it captures what employees are doing now with cameras. Some of the new software ends there, monitoring assembly line workers constantly to make sure that they perform the tasks exactly as designed. Another software called Robotic Process Automation takes those video images and figures out how to redesign tasks to make them more efficient. In other words, it takes over the tasks the workers used to do in lean production (Simonite 2020). Other vendors reassemble jobs to push simpler tasks down to cheaper labour, 9 the classic “deskilling” practice with the classic pushback, that the narrow, simple tasks that result are so boring that engagement, commitment, and performance ultimately decline. They are performing the same tasks that workers had done before with the difference being that now, the most and possibly only interesting part of those jobs is gone. That control is what made the boring jobs tolerable.

More generally, it is also difficult to argue that paying vendors to take over a task that employees either were already doing or could do – updating the performance of tasks through lean production - is going to be cheaper, especially because lean production is a never-ending process that has to be recalibrated whenever there are changes anywhere in the system.

A final especially illustrative example comes from earlier days in IT technology and the introduction of numerically controlled machines in machining work. Here the question was, who will perform the tasks of setting up and programming those machines, something that has to be done frequently, whenever they switch over to a new product or new specifications for it. One option was to hire engineers who were skilled programmers and have them learn the context of machining that was done in different organizations. That would mean getting rid of many of the machinists. The other was to take the machinists who had the knowledge for the latter tasks and teach them programming. It was easier to do the former, but it was far cheaper in the long run to do the latter not only by avoiding the churning costs of laying off one group of workers and hiring in another or even because machinists were paid less than engineers but because the employer then created a cadre of employees with skills unique to them: unlike the programming engineers, who could easily leave for jobs elsewhere, these machinist-programmers now had the best jobs they likely could find anywhere (Kelley 1996).

There is sometimes a view stemming from simple economic assumptions that “firms” always make the most efficient choices because if they do not, they go out of business. But most businesses do fail, and it is possible for larger companies to make the wrong decisions for some time and yet stay in business. There are also so many decisions to be made in businesses that it is inevitable that we will make the wrong ones in some area.

Employers are not rational calculating machines, they are humans with the same limitations in ability to make decisions as all of us have. In the workplace, though, there are systematic reasons why employers might choose the “low road” approach even when alternatives objectively make more sense. One reason is that high road approaches that require engaging employees and soliciting their best efforts are not easy to pursue. They require sustained efforts at communication, building trust, and so forth. Not every business leader has the inclination to pursue that path. Nor do they have the knowledge base to do so. Leaders who come from engineering backgrounds are taught optimization approaches to business problems that, when focused on worker issues, come down to minimizing the costs of using them. That approach per se is not the issue as long as we have complete and accurate measures of costs and benefits 10 . But few if any employers have those measures.

Consider, for example, the cost of turnover, which is one of the most basic facts necessary to operate efficiently. Organizations that are focused on making money need to know what those costs are in order to determine how much investment is efficient to head them off. We also need to know where those costs occur. It is common if they are measured at all to simply count the administrative costs of hiring a replacement. A very careful look at these costs found that even in front-line retail jobs, two-thirds of the costs of turnover come between the time when the employee gives their notice to leave and before they actually depart. That happens in part because of negative effects on peers who remain, in part because of the demands on them of recruiting, hiring, and onboarding replacements. Those costs are massively greater than the administrative costs (Kuhn and Yu 2021). What most employers do instead is use a rough measure of the administrative costs of hiring a new worker as a proxy, which vastly undercounts the true costs. Why employers had not calculated them is in part because it is difficult to do but ultimately because of the unspoken assumption that, unlike say the costs of missing inventory, they are not big enough to bother.

At the same time, employers’ incorrect assumptions can also be explained by a lack of understanding about how humans actually behave. Many employers are simply convinced that in order to be productive the employees must be tightly controlled and refuse to accept the notion that the employees can contribute more when they are given freedom to express their views, contribute to the decision making process and are expected to take initiative 11 .  Another reason, which is investor driven, is the quirkiness of financial accounting: Chief Financial Officers (CFOs) are more likely to invest in software but not in employees because software is an asset that can be depreciated – paid off over time – whereas training and other investments in employees are current expenses that must be paid off completely in the year they are “purchased” (Cappelli 2023). 

To summarize, we offer some practical suggestions on the use of AI in corporate HRM (see Box 1). The choices as to whether to use AI tools or rely on employees depend in part – but only in part - on the nature of the tasks in question. The traditional view that we should automate the simplest tasks is not necessarily the right advice as we saw earlier with lean production where simple tasks were bundled together into jobs that workers largely controlled. There they were able to take over supervisory tasks and proved more adaptable (e.g., they did not need to be reprogrammed) than robots. Beyond the nature of the tasks, the context also determines the choice of using AI or humans.

Box 1. AI and HRM: Q&A

Governments and social partners can come up with a number of policies and practices that help guide corporate HR functions to respond to the AI-related opportunities as well as other technological challenges. Many of them are in line with the ILO-driven human-centred agenda, in particular with its pillars related to “harnessing and managing technology for decent work”, and “universal entitlement to lifelong learning that enables people to acquire skills and to reskill and upskill” 12 (ILO 2019b).

Many governments have been active in promoting a knowledge economy, the development of high-tech firms and technological upgrading in the manufacturing sector through smart manufacturing underpinned by innovations (Cooke, forthcoming). For example, in 2015, the Chinese government launched “Made in China 2025”, which is one of the national strategic initiatives aimed at transitioning China from a “large manufacturing country” to a “strong manufacturing country” through innovations related to digital technology and artificial intelligence (Kania 2019). The success of such a strategic initiative largely depends on the development of a well-educated workforce equipped with the skills and knowledge required by employers. In this case, the industrial policy of making more use of AI went together with upgrading the education and skills of workers.

Technological challenges may imply that workers will experience more transitions – as some jobs get automated. They will need more than ever support to go through a growing number of labour market transitions throughout their lives. In particular, younger workers will need help in “navigating increasingly difficult school-to-work transition” (Cooke, forthcoming). Older workers will need to be able to stay economically active as long as they want. 13 Lifelong learning policies will definitely help to be prepared for these transitions. Interestingly, data science algorithms may actually be useful here first in creating a more efficient labour market for matching workers and jobs and second by making better predictions as to what kind of skills individuals will need next based on their current experiences and jobs.

In this paper we identified some of the key challenges for high-road approach to employee management that are associated with rapid technological development and, in particular, with the use of AI. While the use of AI in HRM, in particular for hiring and work organization, is promising, still low-road approach is rather common and many suboptimal decisions are being made. The situation can be improved by broader employee engagement in HR-related decision-making process, training of managers on the principles and examples of high-road approach, as well as smart government policies. Particular attention should be paid to the development of “knowledge economy”, harnessing and managing technology for decent work, and universal entitlement to lifelong learning that enables people to acquire skills and to reskill and upskill.

As far as research is concerned, we call for more research to be done on:

pluses and minuses of using the AI in HRM;

the “natural boundaries” between the humans and AI;

how to ensure that the AI does not inherit mistakes made by the humans in the past (for example when it comes to hiring);

how AI products can become truly self-learning;

the ways to encourage fruitful collaboration of data scientists and HRM professionals in the development of the AI products; and

the role of policy makers in encouraging the use of “people-friendly” AI and in promoting high-road corporate practices.

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Peter Cappelli is the George W. Taylor Professor of Management at the Wharton School and Director of Wharton’s Center for Human Resources, University of Pennsylvania

Nikolai Rogovsky is a Senior Economist, Research Department, International Labour Office

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ISBN: 9789220394045

https://doi.org/10.54394/OHVV4382

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ILO, SCORE (Sustaining Competitive and Responsible Enterprises): Phase II Final Report 2017, 2017, 36–37. Cited from ILO (2021).

Pareto improvement occurs when a change in allocation does not harm anyone and helps at least one agent, given an initial allocation of goods for a set of agents.

For a very detailed discussion of how machine learning treats hiring tasks as opposed to the more traditional approach from psychology, see Liem et al. (2018).

For a review of this literature, see Van den Bergh et al. (2013).

Bernstein, Kesavan and Staats (2014) note that it is possible to try to balance the recommendations of the algorithms, but for most employers, the reason for using them is to eliminate the time needed for that process.

See, e.g., Lee and DeVoe (2012).

The software is WorkVue. See WTW (n.d.).

This includes intangible costs (such as workers’ views on firm’s reputation as an employer, job quality or equity in decision-making, etc.) that might not be fully addressed or calculated.

As noted earlier these are the two conflicting views of Theory X and Theory Y by Douglas McGregor in his seminal book The Human Side of Enterprise (1960).

Ghosheh, Lee and McCann (2006) provide an overview of the factors that need to be considered for older workers to effectively and constructively continue to contribute to the labour market.

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Regulating Wartime Artificial Intelligence

Gianna hill.

Scholar analyzes potential strategies to regulate wartime use of artificial intelligence.

No longer confined to the realm of science fiction, militarized artificial intelligence (AI) is evolving into warfare. How should international regulators respond?

In a recent paper , Mark Klamberg , a professor at Stockholm University , examines three methods of regulating the use of AI in military operations at the international level. Klamberg suggests that regulators should step up their oversight by using the current international humanitarian law framework, adding AI-specific regulations to existing rules, or developing a new system of regulation altogether.

Militarized AI is not new. Under the Obama Administration, the United States expanded the use of drones. Drones are an example of narrow AI which is AI designed to perform a single task.

The prevalence of sophisticated narrow AI which supports human decision making has increased , as seen in the war in Ukraine. The Ukrainian armed forces developed an Android application to reduce the time spent on targeting artillery. Its algorithm directs human operators to fire at opponents.

But general AI—which performs tasks as well as or better than humans—stands to upend the way war is done. Klamberg argues that the combination of narrow and general AI will increase the speed of warfare and enable quick and efficient decision making in military organizations.

Klamberg explains that current international regulatory efforts have been limited and focus on lethal autonomous weapons systems. The U.S. Department of Defense defines these weapons systems as systems that “select and engage targets” without further human intervention.

But Klamberg suggests that it is misleading to use the term “autonomous” in the context of these weapons systems.

Lethal autonomous weapons systems still incorporate humans either through direct control, supervision, or development of the system, so lethal autonomous weapons systems may still comport with international humanitarian law principles. As the International Committee of the Red Cross explains , the person who had “meaningful human control” over the system is accountable for that weapon.

Because mechanisms to regulate lethal autonomous weapons systems exist, Klamberg instead emphasizes the regulation of AI in military command and control systems. These systems are the organizations of personnel communication and coordination used to accomplish a given military goal.

AI in this context offers many benefits, including improving the accuracy, speed, and scale of decision-making in complex environments in a cost-effective manner.

Klamberg explains that this use of AI may lift the uncertainty of the “fog of war” that results from inefficient communication and information in a military operation. AI technology could connect soldiers and commanders, promoting efficient communication at even the lowest tiers of command.

The use of AI in military command and control systems, however, also poses challenges that regulators should address. Klamberg identifies concerns that the use of AI is more likely to endanger civilians, marks a loss of humanity, and may facilitate biased decision-making. AI may also increase the power asymmetry between nations, creating the potential for riskless warfare where one side is too advanced to fail.

Furthermore, the incorporation of AI into military command and control systems complicates how responsibility is allocated, Klamberg explains . Specifically, who is responsible for AI’s bad decisions? The software programmer, military commander, front-line operator, or even the political leader?

Klamberg identifies a concern that military personnel may be held responsible for the decisions of advanced autonomous systems even though they lack meaningful control over the system. Instead of pinning blame on low-level operators, Klamberg suggests that those overseeing any disciplinary process focus on supervisors and those with more control over the system.

Challengers to militarized AI, also called “abolitionists,” warn against the use of the technology altogether given these risks.

The complexity and rapid development of these technologies makes their regulation at the international level difficult. But the task is a worthwhile endeavor based on Klamberg’s premise that warring nations do not have an unlimited right to injure their enemy.

Klamberg outlines three methods of regulating militarized AI.

Klamberg suggests applying existing rules and principles of international humanitarian law to militarized AI. International humanitarian law is founded on the moral principles of distinction, proportionality, and precaution.

The principle of distinction requires that warring actors distinguish between civilians and combatants. Proportionality entails weighing the cost of harm to civilians against the military advantage of an attack, and the principle of precaution includes taking other measures before an attack to mitigate its adverse effects.

Klamberg claims that these three principles can be programmed into militarized AI and would serve as a regulatory check on the technology. For instance, the principles of distinction and proportionality could be reduced to a formulaic calculation that enables the AI to separate civilians from combatants before executing any action.

To incorporate these principles into AI, Klamberg proposes involving human oversight in AI decisions. Klamberg explains that continuous assessment of the formulas programmed into the AI would serve as reassurance that the AI is acting according to accepted moral principles.

In addition, Klamberg proposes new AI-specific regulation that adds to existing rules, such as the military’s current rules of engagement. These rules are the internal policies that delineate the circumstances under which a military organization will engage in combat.

Klamberg proposes that militarized AI may be constrained through programming which incorporates the rules of engagement. Such programming would either restrict or permit the AI to deploy its weapons consistent with the rules. Klamberg suggests that this ethical programming could become part of the rules of engagement.

Ultimately, Klamberg imagines possible new frameworks for governing militarized AI.

One possibility involves implementing an arms control or trade regime to prevent an AI arms race, such as have been used to control nuclear arms races. As international agreements, arms control and trade regimes disallow the production and sale of certain weapons.

Some of the leading robotics companies have pledged not to weaponize their creations, but Klamberg suggests that these pledges have left companies working with the U.S. Department of Defense noticeable wiggle room. Instead of relying on voluntary pledges, Klamberg calls for the creation of a binding international treaty among countries.

Another possibility includes introducing new regulations governing the methods of AI warfare developed by international bodies in compliance with the Geneva Conventions. But these regulations may be too slow to be effective and may not take into account the development of AI, cautions Klamberg.

Whatever step is taken next, Klamberg suggests it should support an international regulatory framework that adapts to the future challenges of militarized AI.

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