Journal article
Learning to Rank with Top-K Fairness
Transactions on Machine Learning Research, Vol.2025(Sept), pp.1-19
09/20/2025
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.
Details
- Title: Subtitle
- Learning to Rank with Top-K Fairness
- Creators
- Boyang Zhang - Louisiana State UniversityQuanqi Hu - Texas A&M UniversityMingxuan Sun - Louisiana State UniversityQihang Lin - Business Analytics, University of Iowa, United StatesTianbao Yang - Texas A&M University
- Resource Type
- Journal article
- Publication Details
- Transactions on Machine Learning Research, Vol.2025(Sept), pp.1-19
- ISSN
- 2835-8856
- eISSN
- 2835-8856
- Grant note
- 2147253 / National Science Foundation (100000001) 1943486; 2246757; 2147253 / National Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/100000001) 2246757 / National Science Foundation (100000001) National Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/100000001) 1943486 / National Science Foundation (100000001)
- Language
- English
- Date published
- 09/20/2025
- Academic Unit
- Business Analytics
- Record Identifier
- 9985014883102771
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