License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2021.4
URN: urn:nbn:de:0030-drops-138727
URL: https://drops.dagstuhl.de/opus/volltexte/2021/13872/
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Lazar Reich, Claire ; Vijaykumar, Suhas

A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates Be Reconciled?

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LIPIcs-FORC-2021-4.pdf (0.8 MB)


Abstract

Decision makers increasingly rely on algorithmic risk scores to determine access to binary treatments including bail, loans, and medical interventions. In these settings, we reconcile two fairness criteria that were previously shown to be in conflict: calibration and error rate equality. In particular, we derive necessary and sufficient conditions for the existence of calibrated scores that yield classifications achieving equal error rates at any given group-blind threshold. We then present an algorithm that searches for the most accurate score subject to both calibration and minimal error rate disparity. Applied to the COMPAS criminal risk assessment tool, we show that our method can eliminate error disparities while maintaining calibration. In a separate application to credit lending, we compare our procedure to the omission of sensitive features and show that it raises both profit and the probability that creditworthy individuals receive loans.

BibTeX - Entry

@InProceedings{lazarreich_et_al:LIPIcs.FORC.2021.4,
  author =	{Lazar Reich, Claire and Vijaykumar, Suhas},
  title =	{{A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates Be Reconciled?}},
  booktitle =	{2nd Symposium on Foundations of Responsible Computing (FORC 2021)},
  pages =	{4:1--4:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-187-0},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{192},
  editor =	{Ligett, Katrina and Gupta, Swati},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/13872},
  URN =		{urn:nbn:de:0030-drops-138727},
  doi =		{10.4230/LIPIcs.FORC.2021.4},
  annote =	{Keywords: fair prediction, impossibility results, screening decisions, classification, calibration, equalized odds, optimal transport, risk scores}
}

Keywords: fair prediction, impossibility results, screening decisions, classification, calibration, equalized odds, optimal transport, risk scores
Collection: 2nd Symposium on Foundations of Responsible Computing (FORC 2021)
Issue Date: 2021
Date of publication: 31.05.2021


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