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Found 4 papers, 4 papers with code
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
We evaluate RankingSHAP for commonly used learning-to-rank datasets to showcase the more nuanced use of an attribution method while highlighting the limitations of selection-based explanations.
Predictive Uncertainty-based Bias Mitigation in Ranking
Prior work on bias mitigation often assumes that ranking scores, which correspond to the utility that a document holds for a user, can be accurately determined.
Fairness of Exposure in Light of Incomplete Exposure Estimation
In this work, we discuss how to approach fairness of exposure in cases where the policy contains rankings of which, due to inter-item dependencies, we cannot reliably estimate the exposure distribution.
Understanding and Mitigating the Effect of Outliers in Fair Ranking
We formalize outlierness in a ranking, show that outliers are present in realistic datasets, and present the results of an eye-tracking study, showing that users scanning order and the exposure of items are influenced by the presence of outliers.
