Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages

My H. Dinh, James Kotary, Ferdinando Fioretto

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024
PublisherAssociation for Computing Machinery, Inc
Pages2508-2517
Number of pages10
ISBN (Electronic)9798400704505
DOIs
StatePublished - Jun 3 2024
Event2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024 - Rio de Janeiro, Brazil
Duration: Jun 3 2024Jun 6 2024

Publication series

Name2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024

Conference

Conference2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024
Country/TerritoryBrazil
CityRio de Janeiro
Period6/3/246/6/24

Keywords

  • Algorithmic Fairness
  • Learning To Rank
  • Predict-then-optimize

ASJC Scopus subject areas

  • General Business, Management and Accounting

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