Fairness and machine learning
Limitations and Opportunities
EmphBox.
This online textbook is an incomplete work in progress. Essential chapters are still missing. In the spirit of open review, we solicit broad feedback that will influence existing chapters, as well as the development of later material.
| CONTENTS | |||
| About this book | |||
| 1 | Introduction | ||
| 2 | Classification | ||
| We introduce formal non-discrimination criteria, establish their relationships, and illustrate their limitations. | |||
| 3 | Legal background and normative questions | ||
| We survey the literature on discrimination in law, sociology, and philosophy. We then discuss the challenges that arise in translating these ideas of fairness to the statistical decision-making setting. | |||
| 4 | Causality | ||
| We dive into the rich technical repertoire of causal inference and how it helps articulate and address shortcomings of the classification paradigm, while raising new conceptual and normative questions. | |||
| 5 | Testing discrimination in practice | ||
| We systematize tests of discrimination and discuss the practical complexities of applying them, both to traditional decision-making systems and to algorithmic systems. | |||
| 6 | A broader view of discrimination | ||
| 7 | Measurement | ||
| 8 | Algorithmic interventions | ||
| We survey and systematize a burgeoning set of algorithmic interventions aimed at promoting fairness, while highlighting the limitations of this paradigm. | |||
| Appendix: Technical background | |||
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Contact us
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Citations
To cite this book, please use this bibtex entry:
@book{barocas-hardt-narayanan,
title = {Fairness and Machine Learning},
author = {Solon Barocas and Moritz Hardt and Arvind Narayanan},
publisher = {fairmlbook.org},
note = {\url{http://www.fairmlbook.org}},
year = {2019}
}
Last updated: Fri Dec 6 15:49:42 PST 2019