Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
This addresses fairness issues in ranking systems for platforms like job search and social media, but it is incremental as it builds on existing fair LTR models by improving efficiency and integration.
The paper tackles the problem of bias in Learning to Rank (LTR) systems by integrating efficiently-solvable fair ranking models based on Ordered Weighted Average (OWA) optimization into the training loop, achieving a favorable balance between fairness, user utility, and runtime efficiency.
Learning to Rank (LTR) is one of the most widely used machine learning applications. It is a key component in platforms with profound societal impacts, including job search, healthcare information retrieval, and social media content feeds. Conventional LTR models have been shown to produce biases results, stimulating a discourse on how to address the disparities introduced by ranking systems that solely prioritize user relevance. However, while several models of fair learning to rank have been proposed, they suffer from deficiencies either in accuracy or efficiency, thus limiting their applicability to real-world ranking platforms. This paper shows how efficiently-solvable fair ranking models, based on the optimization of Ordered Weighted Average (OWA) functions, can be integrated into the training loop of an LTR model to achieve favorable balances between fairness, user utility, and runtime efficiency. In particular, this paper is the first to show how to backpropagate through constrained optimizations of OWA objectives, enabling their use in integrated prediction and decision models.