Data & Society > Fairness and Abstraction in Sociotechnical Systems

ACM Conference on Fairness, Accountability, and Transparency (FAT*) | 12.05.18

Fairness and Abstraction in Sociotechnical Systems

Andrew D. Selbst, danah boyd, Sorelle Friedler, Suresh Venkatasubramanian, Janet Vertesi

In this paper, authors identify the challenges to integrating fairness into machine learning based systems and suggest next steps.

“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.”

Subscribe to the Data & Society newsletter

Twitter |  Facebook  |  Medium  | RSS

Reporters and media:
[email protected]

General inquiries:
[email protected]

Unless otherwise noted this site and its contents are licensed under a Creative Commons Attribution 3.0 Unported license.  |  Privacy policy