AI Fairness: Designing Equal Opportunity Algorithms
A theory of justice for AI models making decisions about employment, lending, education, criminal justice, and other important social goods.

Decisions about important social goods like education, employment, housing, loans, health care, and criminal justice are all becoming increasingly automated with the help of AI. But because AI models are trained on data with historical inequalities, they often produce unequal outcomes for members of disadvantaged groups. In AI Fairness, Derek Leben draws on traditional philosophical theories of fairness to develop a framework for evaluating AI models, which can be called a theory of algorithmic justice—a theory inspired by the theory of justice developed by the American philosopher John Rawls.

For several years now, researchers who design AI models have investigated the causes of inequalities in AI decisions and proposed techniques for mitigating them. It turns out that in most realistic conditions it is impossible to comply with all metrics simultaneously. Because of this, companies using AI systems will have to choose which metric they think is the correct measure of fairness, and regulators will need to determine how to apply currently existing laws to AI systems. Leben provides a detailed set of practical recommendations for companies looking to evaluate their AI systems and regulators thinking about laws around AI systems, and he offers an honest analysis of the costs of implementing fairness in AI systems—as well as when these costs may or may not be acceptable.
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AI Fairness: Designing Equal Opportunity Algorithms
A theory of justice for AI models making decisions about employment, lending, education, criminal justice, and other important social goods.

Decisions about important social goods like education, employment, housing, loans, health care, and criminal justice are all becoming increasingly automated with the help of AI. But because AI models are trained on data with historical inequalities, they often produce unequal outcomes for members of disadvantaged groups. In AI Fairness, Derek Leben draws on traditional philosophical theories of fairness to develop a framework for evaluating AI models, which can be called a theory of algorithmic justice—a theory inspired by the theory of justice developed by the American philosopher John Rawls.

For several years now, researchers who design AI models have investigated the causes of inequalities in AI decisions and proposed techniques for mitigating them. It turns out that in most realistic conditions it is impossible to comply with all metrics simultaneously. Because of this, companies using AI systems will have to choose which metric they think is the correct measure of fairness, and regulators will need to determine how to apply currently existing laws to AI systems. Leben provides a detailed set of practical recommendations for companies looking to evaluate their AI systems and regulators thinking about laws around AI systems, and he offers an honest analysis of the costs of implementing fairness in AI systems—as well as when these costs may or may not be acceptable.
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AI Fairness: Designing Equal Opportunity Algorithms

AI Fairness: Designing Equal Opportunity Algorithms

by Derek Leben
AI Fairness: Designing Equal Opportunity Algorithms

AI Fairness: Designing Equal Opportunity Algorithms

by Derek Leben

eBook

$38.99 

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Overview

A theory of justice for AI models making decisions about employment, lending, education, criminal justice, and other important social goods.

Decisions about important social goods like education, employment, housing, loans, health care, and criminal justice are all becoming increasingly automated with the help of AI. But because AI models are trained on data with historical inequalities, they often produce unequal outcomes for members of disadvantaged groups. In AI Fairness, Derek Leben draws on traditional philosophical theories of fairness to develop a framework for evaluating AI models, which can be called a theory of algorithmic justice—a theory inspired by the theory of justice developed by the American philosopher John Rawls.

For several years now, researchers who design AI models have investigated the causes of inequalities in AI decisions and proposed techniques for mitigating them. It turns out that in most realistic conditions it is impossible to comply with all metrics simultaneously. Because of this, companies using AI systems will have to choose which metric they think is the correct measure of fairness, and regulators will need to determine how to apply currently existing laws to AI systems. Leben provides a detailed set of practical recommendations for companies looking to evaluate their AI systems and regulators thinking about laws around AI systems, and he offers an honest analysis of the costs of implementing fairness in AI systems—as well as when these costs may or may not be acceptable.

Product Details

ISBN-13: 9780262383226
Publisher: MIT Press
Publication date: 05/13/2025
Sold by: Penguin Random House Publisher Services
Format: eBook
Pages: 240
File size: 6 MB

About the Author

Derek Leben is Associate Teaching Professor of Business Ethics at the Tepper School of Business at Carnegie Mellon University. As founder of the consulting group Ethical Algorithms, he has worked with governments and companies to develop policies on fairness and benefit for AI and autonomous systems.

Table of Contents

Introduction
Chapter 1. The Problem
Chapter 2. Fairness
Chapter 3. AI
Chapter 4. A Theory of Algorithmic Justice
Chapter 5. Equal Treatment
Chapter 6. Relevance
Chapter 7. Equal Impact
Chapter 8. Prices and Wages
Chapter 9. The Cost of Fairness
Epilogue: Sacrifices Not Worth Making

What People are Saying About This

From the Publisher

“Derek Leben provides the first comprehensive account of algorithmic justice: what it means, both in theory and in practice, for an algorithm to support and advance a more just society. As AI fundamentally impacts our lives, communities, and societies, this book provides crucial next steps toward a better future.”
—David Danks, University of California, San Diego

“In AI Fairness, Derek Leben brings together machine learning, ethics, and political philosophy into a practical theoretical framework while exploring numerous real-world case studies. AI Fairness is a thoughtful investigation of one of the central issues of our time.”
—Brian Christian, best-selling author of The Alignment Problem: Machine Learning and Human Values

“Leben provides the philosophical foundation our fairness metrics debate desperately needed, showing technologists why ethical reasoning is essential while guiding philosophers through the intricacies of mathematical formalization.”
—Michele Loi, Senior Scientific Manager, Algorithmwatch.org

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