Understanding and Mitigating the Effect of Outliers in Fair Ranking

Abstract

Traditional ranking systems are expected to sort items in the order of their relevance and thereby maximize their utility. In fair ranking, utility is complemented with fairness as an optimization goal. Recent work on fair ranking focuses on developing algorithms to optimize for fairness, given position-based exposure. In contrast, we identify the potential of outliers in a ranking to influence exposure and thereby negatively impact fairness. An outlier in a list of items can alter the examination probabilities, which can lead to different distributions of attention, compared to position-based exposure. 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. We then introduce OMIT, a method for fair ranking in the presence of outliers. Given an outlier detection method, OMIT improves fair allocation of exposure by suppressing outliers in the top-k ranking. Using an academic search dataset, we show that outlierness optimization leads to a fairer policy that displays fewer outliers in the top-k, while maintaining a reasonable trade-off between fairness and utility.

8 pages, accepted at WSDM'22, full paper track

References in corpus (4)

• Controlling Fairness and Bias in Dynamic Learning-to-Rank
• Unifying Online and Counterfactual Learning to Rank
• When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank
• Policy Learning for Fairness in Ranking

Cited by in corpus (5)

• Fairness of Exposure in Light of Incomplete Exposure Estimation
• Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking
• Predictive Uncertainty-based Bias Mitigation in Ranking
• On the Impact of Outlier Bias on User Clicks
• Learning to Re-rank with Constrained Meta-Optimal Transport