Fairness and Abstraction in Sociotechnical Systems

2019 ACM Conference on Fairness, Accountability, and Transparency (FAT*), 59-68

17 Pages Posted: 7 Nov 2018 Last revised: 30 Jan 2020

See all articles by Andrew D. Selbst

Andrew D. Selbst

UCLA School of Law

Danah Boyd

Microsoft Research; Georgetown University; Data & Society Research Institute

Sorelle Friedler

Haverford College

Suresh Venkatasubramanian

University of Utah

Janet Vertesi

Princeton University

Date Written: August 23, 2018


A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science—such as abstraction and modular design—are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.

Keywords: Fairness-Aware Machine Learning, Sociotechnical Systems, Interdisciplinary

Suggested Citation

Selbst, Andrew D. and Boyd, Danah and Friedler, Sorelle and Venkatasubramanian, Suresh and Vertesi, Janet, Fairness and Abstraction in Sociotechnical Systems (August 23, 2018). 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT*), 59-68, Available at SSRN: https://ssrn.com/abstract=3265913

Danah Boyd

Microsoft Research ( email )

One Memorial Drive, 12th Floor
Cambridge, MA 02142
United States

HOME PAGE: http://research.microsoft.com/

Georgetown University ( email )

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Suite 311
Washington, DC 20057
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Data & Society Research Institute ( email )

36 West 20th Street
11th Floor
New York,, NY 10011
United States

HOME PAGE: http://www.datasociety.net

Sorelle Friedler

Haverford College ( email )

Haverford, PA 19041
United States

Suresh Venkatasubramanian

University of Utah ( email )

1645 E. Campus Center
Salt Lake City, UT 84112
United States

Janet Vertesi

Princeton University ( email )

22 Chambers Street
Princeton, NJ 08544-0708
United States

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