A Framework for Fairness in Two-Sided Marketplaces

Basu, Kinjal; DiCiccio, Cyrus; Logan, Heloise; Karoui, Noureddine El

doi:10.48550/arxiv.2006.12756

Cited by 8 publications

(18 citation statements)

References 21 publications

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“…In the result below, for a ranking tensor P and a user i, we denote by S(P i ) the support of P i in ranking space. 6 Theorem 1.…”

Section: Efficient Inference Of Fair Rankings With the Frank-wolfe Al...mentioning

confidence: 99%

“…In our experiments, we use the standard deviation as a measure of inequality. Denoting by E = |N | v 1 the total exposure and by Q = j∈I q j the total quality: [55,42,6]. D qua and D have qualitatively the same behavior.…”

Section: Objective Functionsmentioning

confidence: 99%

“…The same problem arises with the penalties of previous work. In the literature, authors either considered applying the item-fairness constraints to each ranking individually [54,6], which leads to inefficiencies with our definition of utility (see Appendix H), or resort to heuristics to compute the rankings one by one without guarantees on the trade-offs that are achieved [42,7].…”

Section: Efficient Inference Of Fair Rankings With the Frank-wolfe Al...mentioning

confidence: 99%

“…The leading approach to fairness in rankings is to maximize user utility under constraints of equal item exposure (or equal quality-weighted exposure) [54,7] or equal user satisfaction [6]. When these constraints imply an unacceptable decrease in average user utility, so-called "trade-offs between utility and fairness" [65,41] are obtained by relaxing the fairness constraints, leading to the optimization of a trade-off between average user utility and a measure of users' or items' inequality.…”

Section: Introductionmentioning

confidence: 99%

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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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“…In the result below, for a ranking tensor P and a user i, we denote by S(P i ) the support of P i in ranking space. 6 Theorem 1.…”

Section: Efficient Inference Of Fair Rankings With the Frank-wolfe Al...mentioning

confidence: 99%

Section: Objective Functionsmentioning

confidence: 99%

Section: Efficient Inference Of Fair Rankings With the Frank-wolfe Al...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two-sided fairness in rankings via Lorenz dominance

Do¹,

Corbett-Davies²,

Atif³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Other works on item fairness base their fairness constraints on statistical parity for pairwise ranking across item groups [47][48][49]. In addition to item-based approaches, two-sided fair ranking techniques satisfy fairness constraints for both users and items [50][51][52].…”

Section: Related Workmentioning

confidence: 99%

Fairness for Robust Learning to Rank

Memarrast¹,

Rezaei²,

Fathony³

et al. 2021

Preprint

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While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure for different protected attributes such as gender or race. To achieve this type of group fairness for ranking, we derive a new ranking system based on the first principles of distributional robustness. We formulate a minimax game between a player choosing a distribution over rankings to maximize utility while satisfying fairness constraints against an adversary seeking to minimize utility while matching statistics of the training data. We show that our approach provides better utility for highly fair rankings than existing baseline methods.

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Exploration Trade-offs in Web Recommender Systems

Baeza-Yates,

Delnevo

2022

2022 IEEE International Conference on Big Data (Big Data)

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One of the main problems of web recommender systems is exposure bias, due to the fact that the web system itself is partly generating its own future, as users can only click on items shown to them. This bias not only creates popularity bias for products but also is one of the main challenges for recommender systems that deal with a very dynamic environment, where new items and users appear frequently and also user preferences change (or the market changes as happened with the coronavirus pandemic). The main paradigm to deal with these changes is to explore and exploit, avoiding the filter bubble effect. However, too much exploration also reduces short-term revenue and hence is usually traffic bounded. In this work, we present a counterfactual analysis that shows that web recommender systems could improve their long-term revenue if significantly more exploration is performed. This is good for the web recommender system but also for everyone as it creates more fair and healthy digital markets. This also improves the web user experience so is a double win-win for the e-commerce platform, the sellers, the users, and ultimately society.

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A Framework for Fairness in Two-Sided Marketplaces

Cited by 8 publications

References 21 publications

Two-sided fairness in rankings via Lorenz dominance

Two-sided fairness in rankings via Lorenz dominance

Fairness for Robust Learning to Rank

Exploration Trade-offs in Web Recommender Systems

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