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Metric Learning for Individual Fairness

Author Christina Ilvento



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Author Details

Christina Ilvento
  • Harvard University, John A. Paulson School of Engineering and Applied Science,
  • Cambridge, MA, USA

Acknowledgements

The author is grateful for the comments of Cynthia Dwork.

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Christina Ilvento. Metric Learning for Individual Fairness. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 2:1-2:11, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.FORC.2020.2

Abstract

There has been much discussion concerning how "fairness" should be measured or enforced in classification. Individual Fairness [Dwork et al., 2012], which requires that similar individuals be treated similarly, is a highly appealing definition as it gives strong treatment guarantees for individuals. Unfortunately, the need for a task-specific similarity metric has prevented its use in practice. In this work, we propose a solution to the problem of approximating a metric for Individual Fairness based on human judgments. Our model assumes access to a human fairness arbiter who is free of explicit biases and possesses sufficient domain knowledge to evaluate similarity. Our contributions include definitions for metric approximation relevant for Individual Fairness, constructions for approximations from a limited number of realistic queries to the arbiter on a sample of individuals, and learning procedures to construct hypotheses for metric approximations which generalize to unseen samples under certain assumptions of learnability of distance threshold functions.

Subject Classification

ACM Subject Classification
  • Theory of computation → Machine learning theory
Keywords
  • metric learning
  • individual fairness
  • fair machine learning

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References

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