Fairness and machine learning

Time 2022-08-08 06:49:03

Web Name: Fairness and machine learning

WebSite: http://www.fairmlbook.org

ID:274219

Keywords:

and,Fairness,learning,machine

Description:

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.

CONTENTSPrefaceAcknowledgments1IntroductionPDF2When is automated decision making legitimate?PDFWe explore what makes automated decision making a matter of normative concern, situated in bureaucratic decision making and its mechanical application of formalized rules.3ClassificationPDFWe introduce formal non-discrimination criteria in a decision-theoretic setting, establish their relationships, and illustrate their limitations.4Relative notions of fairnessPDFWe introduce formal non-discrimination criteria in a decision-theoretic setting, establish their relationships, and illustrate their limitations.5CausalityPDFWe 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.6Understanding United States anti-discrimination lawPDFWe discuss what United States anti-discrimination law is and isn’t, how it navigates tradeoffs, its limits, and how it applies to machine learning.7Testing discrimination in practicePDFWe systematize tests of discrimination and discuss the practical complexities of applying them, both to traditional decision-making systems and to algorithmic systems.8A broader view of discriminationPDFWe review structural, organizational, and interpersonal discrimination in society, how machine learning interacts with them, and discuss a broad set of potential interventions.9DatasetsPDFDatasets 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.10Algorithmic interventionsIn this online-only chapter, 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

Berkeley CS 294: Fairness in machine learningCornell INFO 4270: Ethics and policy in data sciencePrinceton COS 597E: Fairness in machine learning

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: Tue Aug 2 04:45:10 PDT 2022

TAGS:and Fairness learning machine

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