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
