Marginal-Certainty-aware Fair Ranking Algorithm

Abstract

Ranking systems are ubiquitous in modern Internet services, including online marketplaces, social media, and search engines. Traditionally, ranking systems only focus on how to get better relevance estimation. When relevance estimation is available, they usually adopt a user-centric optimization strategy where ranked lists are generated by sorting items according to their estimated relevance. However, such user-centric optimization ignores the fact that item providers also draw utility from ranking systems. It has been shown in existing research that such user-centric optimization will cause much unfairness to item providers, followed by unfair opportunities and unfair economic gains for item providers. To address ranking fairness, many fair ranking methods have been proposed. However, as we show in this paper, these methods could be suboptimal as they directly rely on the relevance estimation without being aware of the uncertainty (i.e., the variance of the estimated relevance). To address this uncertainty, we propose a novel Marginal-Certainty-aware Fair algorithm named MCFair. MCFair jointly optimizes fairness and user utility, while relevance estimation is constantly updated in an online manner. In MCFair, we first develop a ranking objective that includes uncertainty, fairness, and user utility. Then we directly use the gradient of the ranking objective as the ranking score. We theoretically prove that MCFair based on gradients is optimal for the aforementioned ranking objective. Empirically, we find that on semi-synthesized datasets, MCFair is effective and practical and can deliver superior performance compared to state-of-the-art fair ranking methods. To facilitate reproducibility, we release our code https://github.com/Taosheng-ty/WSDM22-MCFair.

10 pages, 5 figures

References in corpus (6)

• Controlling Fairness and Bias in Dynamic Learning-to-Rank
• Estimating Position Bias without Intrusive Interventions
• Unifying Online and Counterfactual Learning to Rank
• Maximizing Marginal Fairness for Dynamic Learning to Rank
• Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
• Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank

Cited by in corpus (5)

• Predictive Uncertainty-based Bias Mitigation in Ranking
• Vertical Allocation-based Fair Exposure Amortizing in Ranking
• Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
• FARA: Future-aware Ranking Algorithm for Fairness Optimization
• Fairness in Ranking under Disparate Uncertainty