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- W4283163988 abstract "Welfare measures overall utility across a population, whereas malfare measures overall disutility, and the social planner’s problem can be cast either as maximizing the former or minimizing the latter. We show novel bounds on the expectations and tail probabilities of estimators of welfare, malfare, and regret of per-group (dis)utility values, where estimates are made from a finite sample drawn from each group. In particular, we consider estimating these quantities for individual functions (e.g., allocations or classifiers) with standard probabilistic bounds, and optimizing and bounding generalization error over hypothesis classes (i.e., we quantify overfitting) using Rademacher averages. We then study algorithmic fairness through the lens of sample complexity, finding that because marginalized or minority groups are often understudied, and fewer data are therefore available, the social planner is more likely to overfit to these groups, thus even models that seem fair in training can be systematically biased against such groups. We argue that this effect can be mitigated by ensuring sufficient sample sizes for each group, and our sample complexity analysis characterizes these sample sizes. Motivated by these conclusions, we present progressive sampling algorithms to efficiently optimize various fairness objectives." @default.
- W4283163988 created "2022-06-21" @default.
- W4283163988 creator A5088088780 @default.
- W4283163988 date "2022-06-20" @default.
- W4283163988 modified "2023-10-16" @default.
- W4283163988 title "Uncertainty and the Social Planner’s Problem: Why Sample Complexity Matters" @default.
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- W4283163988 doi "https://doi.org/10.1145/3531146.3533243" @default.
- W4283163988 hasPublicationYear "2022" @default.
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