Fairness and Abstraction in Sociotechnical Systems¶
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59-68.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Procedural Fairness (Due Process)
- Selbst, Boyd, Friedler, Venkatasubramanian, and Vertesi (2019) extend this by arguing that procedurally meaningful fairness requires drawing the system boundary to include human institutions, contestation pathways, and policy processes—not the model alone.
This sourceIdentifies five "abstraction traps" by which fair-ML formalisms misrepresent the sociotechnical context they aim to govern, and argues that procedurally meaningful fairness requires drawing the system boundary to include human institutions, contestation pathways, and policy processes—not the model alone.
- Selbst, Boyd, Friedler, Venkatasubramanian, and Vertesi (2019) extend this by arguing that procedurally meaningful fairness requires drawing the system boundary to include human institutions, contestation pathways, and policy processes—not the model alone.
Mechanisms¶
- Fairness-Standard Comparison Table
- The classic misuse is presenting the table as if picking a column were a neutral, technical act rather than a value judgment — the pseudo-objectivity
This sourceArgues that competing mathematical fairness definitions cannot be chosen by mathematics alone because social context must determine the choice.
- The classic misuse is presenting the table as if picking a column were a neutral, technical act rather than a value judgment — the pseudo-objectivity
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