May 1, 2024
Updated June 27, 2025
14 minute read
An Introduction to Fairness in Artificial Intelligence
Artificial Intelligence, or AI, refers to the simulation of human intelligence in machines that are programmed to think, learn, and problem-solve. These systems are increasingly integrated into our daily lives, influencing everything from the news we read to the music we hear. As AI's role expands, ensuring these technologies operate equitably has become a paramount concern. This field of study and practice is known as "Fairness in AI." At its core, it is about identifying and mitigating unjust or prejudicial treatment of individuals or groups in the outcomes of automated decisions.
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Find a path to becoming a Fairness in AI. Learn more at:
OpenCourser.com/topic/q2umrl/fairness
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Fairness in AI.
Provides a comprehensive overview of the ethical issues surrounding AI, including fairness, transparency, accountability, and autonomy. Liao draws on a range of philosophical perspectives to explore the ethical implications of AI and offers practical guidance on how to develop and use AI systems in a responsible manner.
Examines the ways in which AI systems can perpetuate and amplify existing social biases. Noble argues that AI systems are not neutral but rather reflect and reinforce the values and assumptions of the people who create them. She calls for a more critical and ethical approach to the development and use of AI systems.
Explores the future of fairness in AI. O'Neil argues that we need to develop new ways of thinking about fairness and to create new institutions to ensure that AI systems are used in a fair and responsible manner.
Provides a concise and accessible overview of fairness in AI. Russell argues that fairness fundamental ethical principle that should be considered in the development and use of AI systems.
Provides a practical guide to identifying and mitigating bias in AI systems. Thomas offers a range of tools and techniques that can be used to assess the fairness of AI systems and to reduce bias.
Provides a comprehensive overview of responsible AI, covering topics such as fairness, transparency, accountability, and safety. Dignum argues that we need to develop a new approach to AI that is based on human values.
Explores the ethical challenges of AI, including fairness, transparency, and accountability. Kearns and Roth argue that we need to develop new ethical frameworks for AI that are based on human values.
Provides a critical examination of the hype surrounding AI. Whittaker argues that AI is not a panacea for the world's problems and that we need to be realistic about its limitations.
Explores the ways in which AI is being used to automate inequality. Eubanks argues that AI is being used to create new forms of discrimination and to exacerbate existing social inequalities.
Explores the ways in which AI is being used to create a new form of surveillance capitalism. Zuboff argues that AI is being used to track and control our every move, and she calls for a new era of privacy and data protection.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/q2umrl/fairness
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