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Advancing Subgroup Fairness via Sleeping Experts

Authors Avrim Blum, Thodoris Lykouris

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Author Details

Avrim Blum
  • Toyota Technological Institute at Chicago, IL, United States
Thodoris Lykouris
  • Microsoft Research NYC, United States

Funding

  • Blum, Avrim: The author was supported in part by NSF grants CCF-1815011 and CCF-1733556.
  • Lykouris, Thodoris: Research initiated during the author’s visit to TTI-Chicago while he was a Ph.D. student at Cornell University. The author was supported in part under NSF grant CCF-1563714 and a Google Ph.D. fellowship.

Acknowledgements

The authors would like to thank Suriya Gunasekar for various useful discussions during the initial stages of this work and Manish Raghavan for offering comments in a preliminary version of the work.

Cite AsGet BibTex

Avrim Blum and Thodoris Lykouris. Advancing Subgroup Fairness via Sleeping Experts. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 151, pp. 55:1-55:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.ITCS.2020.55

Abstract

We study methods for improving fairness to subgroups in settings with overlapping populations and sequential predictions. Classical notions of fairness focus on the balance of some property across different populations. However, in many applications the goal of the different groups is not to be predicted equally but rather to be predicted well. We demonstrate that the task of satisfying this guarantee for multiple overlapping groups is not straightforward and show that for the simple objective of unweighted average of false negative and false positive rate, satisfying this for overlapping populations can be statistically impossible even when we are provided predictors that perform well separately on each subgroup. On the positive side, we show that when individuals are equally important to the different groups they belong to, this goal is achievable; to do so, we draw a connection to the sleeping experts literature in online learning. Motivated by the one-sided feedback in natural settings of interest, we extend our results to such a feedback model. We also provide a game-theoretic interpretation of our results, examining the incentives of participants to join the system and to provide the system full information about predictors they may possess. We end with several interesting open problems concerning the strength of guarantees that can be achieved in a computationally efficient manner.

Subject Classification

ACM Subject Classification
  • Theory of computation → Online learning algorithms
Keywords
  • Online learning
  • Fairness
  • Game theory

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