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Fairness Under Composition

Authors Cynthia Dwork, Christina Ilvento

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Author Details

Cynthia Dwork
  • Harvard John A Paulson School of Engineering and Applied Science, Radcliffe Institute for Advanced Study, Cambridge, MA, USA
Christina Ilvento
  • Harvard John A Paulson School of Engineering and Applied Science, Cambridge, MA, USA

Funding

  • Dwork, Cynthia: This work was supported in part by Microsoft Research and the Sloan Foundation.
  • Ilvento, Christina: This work was supported in part by the Smith Family Fellowship and Microsoft Research.

Cite AsGet BibTex

Cynthia Dwork and Christina Ilvento. Fairness Under Composition. In 10th Innovations in Theoretical Computer Science Conference (ITCS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 124, pp. 33:1-33:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ITCS.2019.33

Abstract

Algorithmic fairness, and in particular the fairness of scoring and classification algorithms, has become a topic of increasing social concern and has recently witnessed an explosion of research in theoretical computer science, machine learning, statistics, the social sciences, and law. Much of the literature considers the case of a single classifier (or scoring function) used once, in isolation. In this work, we initiate the study of the fairness properties of systems composed of algorithms that are fair in isolation; that is, we study fairness under composition. We identify pitfalls of naïve composition and give general constructions for fair composition, demonstrating both that classifiers that are fair in isolation do not necessarily compose into fair systems and also that seemingly unfair components may be carefully combined to construct fair systems. We focus primarily on the individual fairness setting proposed in [Dwork, Hardt, Pitassi, Reingold, Zemel, 2011], but also extend our results to a large class of group fairness definitions popular in the recent literature, exhibiting several cases in which group fairness definitions give misleading signals under composition.

Subject Classification

ACM Subject Classification
  • Theory of computation → Computational complexity and cryptography
  • Theory of computation → Design and analysis of algorithms
  • Theory of computation → Theory and algorithms for application domains
Keywords
  • algorithmic fairness
  • fairness
  • fairness under composition

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References

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