Designing Fair Ranking Schemes

Abstract

Items from a database are often ranked based on a combination of multiple criteria. A user may have the flexibility to accept combinations that weigh these criteria differently, within limits. On the other hand, this choice of weights can greatly affect the fairness of the produced ranking. In this paper, we develop a system that helps users choose criterion weights that lead to greater fairness. We consider ranking functions that compute the score of each item as a weighted sum of (numeric) attribute values, and then sort items on their score. Each ranking function can be expressed as a vector of weights, or as a point in a multi-dimensional space. For a broad range of fairness criteria, we show how to efficiently identify regions in this space that satisfy these criteria. Using this identification method, our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and, if it does not, to suggest the smallest modification that does. We develop user-controllable approximation that and indexing techniques that are applied during preprocessing, and support sub-second response times during the online phase. Our extensive experiments on real datasets demonstrate that our methods are able to find solutions that satisfy fairness criteria effectively and efficiently.

References in corpus (6)

• FA*IR: A Fair Top-k Ranking Algorithm
• On the (im)possibility of fairness
• Runaway Feedback Loops in Predictive Policing
• RRR: Rank-Regret Representative
• Query Reranking As A Service
• Discovering the Skyline of Web Databases

Cited by in corpus (24)

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• Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
• FairSight: Visual Analytics for Fairness in Decision Making
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• User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets
• Policy Learning for Fairness in Ranking
• Inequality and Inequity in Network-based Ranking and Recommendation Algorithms
• On Obtaining Stable Rankings
• RRR: Rank-Regret Representative
• Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
• Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking
• Toward Controlling Discrimination in Online Ad Auctions
• FAIR: Fairness-Aware Information Retrieval Evaluation
• Happiness Maximizing Sets under Group Fairness Constraints (Technical Report)
• Consequential Ranking Algorithms and Long-term Welfare
• FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning
• Fairness Through Regularization for Learning to Rank
• Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
• Fairness in Ranking under Uncertainty
• Fairness-Aware Online Personalization
• Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions
• Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations
• Understanding the impact of the alphabetical ordering of names in user interfaces: a gender bias analysis