Modern learning to rank systems have achieved remarkable performance across a wide range of applications. However, they may also exhibit disparities in exposure, raising concerns about fairness, especially in sensitive domains such as healthcare, judicial decision-making, and recruitment, where biased rankings may have...
Tristan Cladière, Antoine Gourru, Bissan Audeh et al.· Proceedings of the 20th ACM...· 0 citations
Fairness in ranking is studied by leveraging threshold-based constraints on disparate exposure, which, under a distributional approximation, induce a predictable transformation of the exposure distribution, providing novel and sound approach to tuning fairness in ranking systems.
Tristan Cladière, Antoine Gourru, Bissan Audeh et al.· Proceedings of the 20th ACM...· 0 citations
Debias-SparseGPT is introduced, a post-training pruning method incorporating representational debiasing using a second-order term defined over demographically contrasting inputs that consistently reduces pruning-induced bias compared to SparseGPT while preserving model perplexity and zero-shot accuracy.
Irina Proskurina, Guillaume Metzler, Antoine Gourru et al.· 0 citations
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