New Approach to Fair Top-k Selection with Multiple Protected Groups
Researchers have published a study on arXiv proposing a generalized framework for fair top-k selection that handles multiple protected groups while minimizing scoring disparity. Prior work was limited to single-group settings and overlooked a critical computational issue that can render the problem intractable even in simple cases. The work matters because fair ranking algorithms are increasingly used in high-stakes decisions such as hiring and admissions, and handling multiple protected groups efficiently is essential for real-world applicability.
A new preprint on arXiv (cs.DS/cs.LG) introduces a generalized approach to fair top-k selection, a problem concerned with ensuring proportional representation of minority or historically disadvantaged groups among the top-k candidates selected by a scoring function. The authors identify a critical flaw overlooked in prior work: when multiple protected groups are considered, the problem can become computationally intractable even for two-dimensional datasets and small values of k. Despite this hardness result, the researchers identify a gap in the hardness barrier that allows efficient solutions when both k and the number of protected groups are sufficiently small. The paper also introduces an alternative disparity measure called 'utility loss,' which is argued to produce more stable scoring functions under small perturbations to scoring weights compared to the traditional distance-based measure. The authors present a two-pronged algorithmic solution that balances implementation complexity, robustness, and performance, demonstrating strong empirical results on real-world datasets. The work spans multiple subfields including computational complexity, computational geometry, databases, and machine learning.
What's missing
The paper is a preprint and has not yet undergone formal peer review. Open questions include how the utility-loss disparity measure behaves under adversarial perturbations and whether the efficiency guarantees hold as the number of protected groups scales beyond the 'sufficiently small' regime.
What different sources said
- arXiv cs.LGCenter
Generalizing Fair Top-$k$ Selection: An Integrative Approach
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