Related papers: Individually Fair Ranking

Individually Fair Ranking

URL: http://arxiv.org/abs/2103.11023v1
Date: Fri, 19 Mar 2021 21:17:11 GMT
Title: Individually Fair Ranking
Authors: Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun
Abstract summary: We develop an algorithm to train individually fair learning-to-rank models. The proposed approach ensures items from minority groups appear alongside similar items from majority groups.
Score: 23.95661284311917
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from majority groups. This notion of fair ranking is based on the definition of individual fairness from supervised learning and is more nuanced than prior fair LTR approaches that simply ensure the ranking model provides underrepresented items with a basic level of exposure. The crux of our method is an optimal transport-based regularizer that enforces individual fairness and an efficient algorithm for optimizing the regularizer. We show that our approach leads to certifiably individually fair LTR models and demonstrate the efficacy of our method on ranking tasks subject to demographic biases.

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