Individually Fair Ranking

Abstract

We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from majority groups. This notion of fair ranking is based on the definition of individual fairness from supervised learning and is more nuanced than prior fair LTR approaches that simply ensure the ranking model provides underrepresented items with a basic level of exposure. The crux of our method is an optimal transport-based regularizer that enforces individual fairness and an efficient algorithm for optimizing the regularizer. We show that our approach leads to certifiably individually fair LTR models and demonstrate the efficacy of our method on ranking tasks subject to demographic biases.

ICLR Camera-Ready Version

References in corpus (5)

• Equality of Opportunity in Supervised Learning
• Two Simple Ways to Learn Individual Fairness Metrics from Data
• On conditional parity as a notion of non-discrimination in machine learning
• An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision
• Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach