Fairness and machine learning
Limitations and Opportunities
Solon Barocas, Moritz Hardt, Arvind Narayanan
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. Please contact us at contact@fairmlbook.org.
| 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 | ||
| We review structural, organizational, and interpersonal discrimination in society, how machine learning interacts with them, and discuss a broad set of potential interventions. | |||
| 7 | Datasets | ||
| Datasets are the backbone of machine learning research and development. We critically examine their role, the harms associated with data, and survey improvements in data practices. | |||
| 8 | Algorithmic interventions | ||
| We survey and systematize a burgeoning set of algorithmic interventions aimed at promoting fairness, while highlighting the limitations of this paradigm. | |||
Video tutorials
- Fairness and Machine Learning (Part 1, Part 2) (MLSS 2020)
- Fairness in machine learning (NeurIPS 2017)
- 21 fairness definitions and their politics (FAccT 2018)
Course materials
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: Wed 16 Jun 2021 01:38:26 PM PDT