Learning Fair Ranking Policies via Differentiable Optimization of
  Ordered Weighted Averages
My H. Dinh James Kotary Ferdinando Fioretto
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.