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On Fairness and Stability in Two-Sided Matchings

Authors Gili Karni, Guy N. Rothblum, Gal Yona

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Author Details

Gili Karni
  • Weizmann Institute of Science, Rehovot, Israel
Guy N. Rothblum
  • Weizmann Institute of Science, Rehovot, Israel
Gal Yona
  • Weizmann Institute of Science, Rehovot, Israel

Funding

  • Karni, Gili: Research supported by the Israel Science Foundation (grant number 5219/17), and by the Simons Foundation Collaboration on the Theory of Algorithmic Fairness.
  • Rothblum, Guy N.: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 819702), from the Israel Science Foundation (grant number 5219/17), and from the Simons Foundation Collaboration on the Theory of Algorithmic Fairness.
  • Yona, Gal: Supported by the European Research Council (ERC) (grant agreement No. 819702), by the Israel Science Foundation (grant number 5219/17), by the Simons Foundation Collaboration on the Theory of Algorithmic Fairness, by the Israeli Council for Higher Education via the Weizmann Data Science Research Center, by a Google PhD fellowship, and by a grant from the Estate of Tully and Michele Plesser.

Acknowledgements

We thank Shahar Dobzinski and Moni Naor for helpful conversations and suggestions about this work and its presentation. We also thank the anonymous ITCS 2022 reviewers for their feedback.

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Gili Karni, Guy N. Rothblum, and Gal Yona. On Fairness and Stability in Two-Sided Matchings. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 92:1-92:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.ITCS.2022.92

Abstract

There are growing concerns that algorithms, which increasingly make or influence important decisions pertaining to individuals, might produce outcomes that discriminate against protected groups. We study such fairness concerns in the context of a two-sided market, where there are two sets of agents, and each agent has preferences over the other set. The goal is producing a matching between the sets. Throughout this work, we use the example of matching medical residents (who we call "doctors") to hospitals. This setting has been the focus of a rich body of work. The seminal work of Gale and Shapley formulated a stability desideratum, and showed that a stable matching always exists and can be found in polynomial time. 
With fairness concerns in mind, it is natural to ask: might a stable matching be discriminatory towards some of the doctors? How can we obtain a fair matching? The question is interesting both when hospital preferences might be discriminatory, and also when each hospital’s preferences are fair. 
We study this question through the lens of metric-based fairness notions (Dwork et al. [ITCS 2012] and Kim et al. [ITCS 2020]). We formulate appropriate definitions of fairness and stability in the presence of a similarity metric, and ask: does a fair and stable matching always exist? Can such a matching be found in polynomial time? Can classical Gale-Shapley algorithms find such a matching? Our contributions are as follows:  
- Composition failures for classical algorithms. We show that composing the Gale-Shapley algorithm with fair hospital preferences can produce blatantly unfair outcomes. 
- New algorithms for finding fair and stable matchings. Our main technical contributions are efficient new algorithms for finding fair and stable matchings when: (i) the hospitals' preferences are fair, and (ii) the fairness metric satisfies a strong "proto-metric" condition: the distance between every two doctors is either zero or one. In particular, these algorithms also show that, in this setting, fairness and stability are compatible. 
- Barriers for finding fair and stable matchings in the general case. We show that if the hospital preferences can be unfair, or if the metric fails to satisfy the proto-metric condition, then no algorithm in a natural class can find a fair and stable matching. The natural class includes the classical Gale-Shapley algorithms and our new algorithms.

Subject Classification

ACM Subject Classification
  • Theory of computation → Theory and algorithms for application domains
Keywords
  • algorithmic fairness

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