Ranking with Fairness Constraints

Celis, L. Elisa; Straszak, Damian; Vishnoi, Nisheeth K.

doi:10.48550/arxiv.1704.06840

Cited by 41 publications

(27 citation statements)

References 24 publications

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“…(2018) and references therein. Our paper adds to the literature on fair methods for unsupervised learning tasks (Chierichetti et al, 2017;Celis et al, 2017;Samadi et al, 2018;Tantipongpipat et al, 2019;Oneto and Chiappa, 2020;Caton and Haas, 2020;Kleindessner et al, 2019). We discuss the work on fairness most closely related to our paper.…”

Section: Related Workmentioning

confidence: 81%

Fair Structure Learning in Heterogeneous Graphical Models

Tarzanagh¹,

Balzano²,

Hero³

2021

Preprint

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Inference of community structure in probabilistic graphical models may not be consistent with fairness constraints when nodes have demographic attributes. Certain demographics may be over-represented in some detected communities and under-represented in others. This paper defines a novel 1 -regularized pseudo-likelihood approach for fair graphical model selection. In particular, we assume there is some community or clustering structure in the true underlying graph, and we seek to learn a sparse undirected graph and its communities from the data such that demographic groups are fairly represented within the communities. Our optimization approach uses the demographic parity definition of fairness, but the framework is easily extended to other definitions of fairness. We establish statistical consistency of the proposed method for both a Gaussian graphical model and an Ising model for, respectively, continuous and binary data, proving that our method can recover the graphs and their fair communities with high probability.

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Section: Related Workmentioning

confidence: 81%

Fair Structure Learning in Heterogeneous Graphical Models

Tarzanagh¹,

Balzano²,

Hero³

2021

Preprint

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show abstract

“…To address this challenge, multiple notions of fairness in ranking have been developed. Celis et al [6] propose to directly require fair representation between groups within each prefix of a ranking, by specifying a mixed integer programming problem to solve for rankings of the desired form. Zehlike et al [22] design a greedy randomized algorithm to produce rankings which satisfy fairness up to a threshold of statistical significance.…”

Section: Related Workmentioning

confidence: 99%

End-to-end Learning for Fair Ranking Systems

Kotary¹,

Fioretto²,

Hentenryck³

et al. 2021

Preprint

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The learning-to-rank problem aims at ranking items to maximize exposure of those most relevant to a user query. A desirable property of such ranking systems is to guarantee some notion of fairness among specified item groups. While fairness has recently been considered in the context of learning-to-rank systems, current methods cannot provide guarantees on the fairness of the proposed ranking policies. This paper addresses this gap and introduces Smart Predict and Optimize for Fair Ranking (SPOFR), an integrated optimization and learning framework for fairness-constrained learning to rank. The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints, while allowing for fine control of the fairness-utility tradeoff. SPOFR is shown to significantly improve current state-of-the-art fair learning-to-rank systems with respect to established performance metrics.

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“…The question of fairness in rankings originated from independent audits on recommender systems or search engines, which showed that results could exhibit bias against relevant social groups [57,33,21,40,35] Our work follows the subsequent work on ranking algorithms that promote fairness of exposure for individual or sensitive groups of items [10,8,7,54,42,65]. The goal is often to prevent winner-take-all effects, combat popularity bias [1] or promote smaller producers [39,41].…”

Section: Related Workmentioning

confidence: 99%

Two-sided fairness in rankings via Lorenz dominance

Do¹,

Corbett-Davies²,

Atif³

et al. 2021

Preprint

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We consider the problem of generating rankings that are fair towards both users and item producers in recommender systems. We address both usual recommendation (e.g., of music or movies) and reciprocal recommendation (e.g., dating). Following concepts of distributive justice in welfare economics, our notion of fairness aims at increasing the utility of the worse-off individuals, which we formalize using the criterion of Lorenz efficiency. It guarantees that rankings are Pareto efficient, and that they maximally redistribute utility from better-off to worse-off, at a given level of overall utility. We propose to generate rankings by maximizing concave welfare functions, and develop an efficient inference procedure based on the Frank-Wolfe algorithm. We prove that unlike existing approaches based on fairness constraints, our approach always produces fair rankings. Our experiments also show that it increases the utility of the worse-off at lower costs in terms of overall utility.35th Conference on Neural Information Processing Systems (NeurIPS 2021).

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Ranking with Fairness Constraints

Cited by 41 publications

References 24 publications

Fair Structure Learning in Heterogeneous Graphical Models

Fair Structure Learning in Heterogeneous Graphical Models

End-to-end Learning for Fair Ranking Systems

Two-sided fairness in rankings via Lorenz dominance

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