Designing Fair Ranking Schemes

By Abolfazl Asudeh, Hosagrahar Visvesvaraya Jagadish, Julia Stoyanovich, Gautam Das. 2017

180 citations recorded by the source corpus.

Items from a database are often ranked based on a combination of criteria. The weight given to each criterion in the combination can greatly affect the fairness of the produced ranking, for example, preferring men over women. A user may have the flexibility to choose combinations that weigh these criteria differently, within limits. In this paper, we develop a system that helps users choose criterion weights that lead to greater fairness. We consider ranking functions that compute the score of each item as a weighted sum of (numeric) attribute values, and then sort items on their score. Each ranking function can be expressed as a point in a multi-dimensional space. For a broad range of fairness criteria, including proportionality, we show how to efficiently identify regions in this space that satisfy these criteria. Using this identification method, our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and, if it does not, to suggest the smallest modification that does. Our extensive experiments on real datasets demonstrate that our methods are able to find solutions that satisfy fairness criteria effectively (usually with only small changes to proposed weight vectors) and efficiently (in interactive time, after some initial pre-processing).

Subfields: Artificial Intelligence, Transportation, Signal Processing

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Citations in the corpus

FairSight: Visual Analytics for Fairness in Decision Making (2019, background): “Recently, fair ranking methods have been proposed [2, 41, 23].”
Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Towards individual equityto-attention fairness in rankings, previous work [115], [116] propose multiple ranking functions to sort items and then achieve fairness amortized across these rankings.”
Toward Controlling Discrimination in Online Ad Auctions (2019, unknown): “ing [10,24] who control polarization in social-networks and personalized feeds, [42] who diversify personal feeds, and [12] who create a diverse and balanced summary of a set of results. In addition, [6,13,44] study fair ranking algorithms; these could be used to generate a balanced list of results ”
Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations (2018, background): “Asudehy et al. [2] proposed a system that assists users in choosing weights that induce fairness into the ranking.”
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach (2020, background): “This requires that fairness constraints be crafted very carefully (which is a non-trivial problem (Asudeh et al., 2019)) so as to not have an undue impact on user welfare.”
Fairness of Exposure in Rankings (2018, background): “[1], have also looked at the task of designing fair scoring functions that satisfy desirable fairness constraints.”
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search (2019, methodology): “With respect to a xed set of items, given a weight vector for ranking, the method in [5] computes a similar vector that meets fairness requirements.”