Fairness and machine learning

Limitations and Opportunities

Solon Barocas, Moritz Hardt, Arvind Narayanan

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 PDF
2 Classification PDF
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 PDF
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 PDF
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 PDF

PDF version (all available chapters)

Video tutorials

Course materials

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