Fairness and Machine Learning
Limitations and Opportunities
340 pp., 7 x 9 in, 40 b&w illus.
- Published: November 28, 2023
- Publisher: The MIT Press
An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning.
Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of an emerging set of proposed solutions, showing how even well-intentioned applications may give rise to objectionable results. It covers the statistical and causal measures used to evaluate the fairness of machine learning models as well as the procedural and substantive aspects of decision-making that are core to debates about fairness, including a review of legal and philosophical perspectives on discrimination. This incisive textbook prepares students of machine learning to do quantitative work on fairness while reflecting critically on its foundations and its practical utility.
• Introduces the technical and normative foundations of fairness in automated decision-making
• Covers the formal and computational methods for characterizing and addressing problems
• Provides a critical assessment of their intellectual foundations and practical utility
• Features rich pedagogy and extensive instructor resources
This is the textbook I've been waiting for. This incisive exploration offers a new generation of machine learning practitioners the tools to critically reflect on machine learning by drawing on diverse disciplinary perspectives.
Ruha Benjamin, Professor of African American Studies, Princeton University; author of Race After Technology: Abolitionist Tools for the New Jim Code
Studying automated decision-making requires an understanding of both algorithms and their interaction with the broader social context. This book provides a canonical introduction to this interdisciplinary field for students, instructors, and practitioners.
Boaz Barak, Professor of Computer Science, Harvard University