Fairness and abstraction in sociotechnical systems

Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, Janet Vertesi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

50 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationFAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency
PublisherAssociation for Computing Machinery, Inc
Pages59-68
Number of pages10
ISBN (Electronic)9781450361255
DOIs
StatePublished - Jan 29 2019
Event2019 ACM Conference on Fairness, Accountability, and Transparency, FAT* 2019 - Atlanta, United States
Duration: Jan 29 2019Jan 31 2019

Publication series

NameFAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency

Conference

Conference2019 ACM Conference on Fairness, Accountability, and Transparency, FAT* 2019
CountryUnited States
CityAtlanta
Period1/29/191/31/19

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Engineering(all)

Keywords

  • Fairness-aware Machine Learning
  • Interdisciplinary
  • Sociotechnical Systems

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  • Cite this

    Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency (pp. 59-68). (FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency). Association for Computing Machinery, Inc. https://doi.org/10.1145/3287560.3287598