Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages

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 …

applications biases cs.ai cs.cy cs.lg differentiable discourse fair healthcare impacts information job key machine machine learning machine learning applications media optimization platforms ranking retrieval search social social media via