New Approach to Fair Top-k Selection with Multiple Protected Groups
Researchers developed an improved method for fair top-k selection that handles multiple protected groups while minimizing deviation from a reference scoring function. The work reveals that the problem becomes computationally harder than previously thought when multiple groups are considered, but identifies algorithmic solutions for practical cases. This advances fairness in algorithmic selection systems used in hiring, admissions, and other high-stakes decisions.
A new study on arXiv addresses the challenge of fair top-k selection—ensuring proportional representation of minority or disadvantaged groups in ranked selections—while also maintaining similarity to an existing scoring function. The researchers generalize prior work that only handled single protected groups, extending the framework to multiple groups simultaneously. Their theoretical analysis reveals a critical computational complexity issue: the problem becomes NP-hard even for two-dimensional datasets with small k values, contradicting earlier assumptions about scalability. However, the authors identify a computational gap that allows efficient solutions when k is small and the number of protected groups is limited. They also introduce an alternative disparity measure called "utility loss" that produces more stable scoring functions under weight perturbations. Empirical validation on real-world datasets demonstrates strong performance, with insights from experiments informing the algorithm's design and implementation.
What's missing
The study's own limitations and open questions include: the restriction to linear scoring functions; the computational intractability for large k or many protected groups; and whether the utility loss measure generalizes effectively across diverse real-world fairness contexts beyond those tested.
What different sources said
- arXiv cs.LGCenter
Generalizing Fair Top-$k$ Selection: An Integrative Approach
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