Learning to Rank with Top-$K$ Fairness

• URL: http://arxiv.org/abs/2509.18067v1
• Date: Mon, 22 Sep 2025 17:47:10 GMT
• Title: Learning to Rank with Top-$K$ Fairness
• Authors: Boyang Zhang, Quanqi Hu, Mingxuan Sun, Qihang Lin, Tianbao Yang,
• Abstract summary: Most fairness-aware ranking systems focus on ensuring comparable average exposure for groups across the entire ranked list.<n>We propose a list-wise learning-to-rank framework that addresses the issues of inequalities in top-$K$ rankings at training time.
• Score: 44.93371887251112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fairness in ranking models is crucial, as disparities in exposure can disproportionately affect protected groups. Most fairness-aware ranking systems focus on ensuring comparable average exposure for groups across the entire ranked list, which may not fully address real-world concerns. For example, when a ranking model is used for allocating resources among candidates or disaster hotspots, decision-makers often prioritize only the top-$K$ ranked items, while the ranking beyond top-$K$ becomes less relevant. In this paper, we propose a list-wise learning-to-rank framework that addresses the issues of inequalities in top-$K$ rankings at training time. Specifically, we propose a top-$K$ exposure disparity measure that extends the classic exposure disparity metric in a ranked list. We then learn a ranker to balance relevance and fairness in top-$K$ rankings. Since direct top-$K$ selection is computationally expensive for a large number of items, we transform the non-differentiable selection process into a differentiable objective function and develop efficient stochastic optimization algorithms to achieve both high accuracy and sufficient fairness. Extensive experiments demonstrate that our method outperforms existing methods.

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