Tradeoffs and Limitations in Algorithmic Fairness @SimonsInstitute
Tradeoffs and Limitations in Algorithmic Fairness  @SimonsInstitute
Uploaded February 2026 | Updated September 2026, 2 weeks ago
Etam Benger (Hebrew University of Jerusalem)
https://simons.berkeley.edu/talks/etam-benger-hebrew-university-jerusalem-2026-02-12
Theory of Computing and Healthcare

Two central criteria of group fairness -- error-rate parity and calibration within subgroups -- usually cannot be satisfied at the same time. We introduce a natural way to relax these notions, which reveals the tradeoffs between them and suggests a range of new, optimally fair prediction rules. This relaxation also highlights when some fairness requirements must fail. Finally, we turn to the surprising question of when and how it is even possible to make fair decisions based on fair scores.
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Simons Institute for the Theory of Computing |

Tradeoffs and Limitations in Algorithmic Fairness

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