Generalizing Fair Top-$k$ Selection: An Integrative Approach
Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention. We study the problem of finding a fair (linear) scoring function with multiple protected groups while also minimizing the disparity from a reference scoring function. This generalizes the prior setup, which was restricted to the single-group setting without disparity minimization. Previous studies imply that the number of protected groups may have a limited impact on the runtime efficiency. However, driven by the need for experimental exploration, we find that this implication ove...
| Authors | Guangya Cai |
|---|---|
| Primary category | cs.DS |
| Published | 5 March 2026 |
| Last updated at source | 9 June 2026 |
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