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Evidence record 10604 · automatically gathered

Price of Fairness in Bandits: A Tight Minimax Characterization

In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as clinical trials. Recent work addresses this by evaluating the sequence of per-round expected rewards through the generalized $p$-mean, interpolating between utilitarian welfare ($p=1$), Nash welfare ($p\to0$), and Rawlsian fairness ($p\to-\infty$). Although tight guarantees are known for $p\ge0$, the strictly fair regim

Record details

Published: 15 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare
Retrieved: 16 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (15 July 2026), “Price of Fairness in Bandits: A Tight Minimax Characterization,” evidence record 10604, https://ethics.ai/record/10604 (originally published by arXiv).

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