License: Creative Commons Attribution 3.0 Unported license (CC-BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2020.1
URN: urn:nbn:de:0030-drops-120179
URL: https://drops.dagstuhl.de/opus/volltexte/2020/12017/
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Cohen, Lee ; Lipton, Zachary C. ; Mansour, Yishay

Efficient Candidate Screening Under Multiple Tests and Implications for Fairness

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LIPIcs-FORC-2020-1.pdf (0.6 MB)


Abstract

When recruiting job candidates, employers rarely observe their underlying skill level directly. Instead, they must administer a series of interviews and/or collate other noisy signals in order to estimate the worker’s skill. Traditional economics papers address screening models where employers access worker skill via a single noisy signal. In this paper, we extend this theoretical analysis to a multi-test setting, considering both Bernoulli and Gaussian models. We analyze the optimal employer policy both when the employer sets a fixed number of tests per candidate and when the employer can set a dynamic policy, assigning further tests adaptively based on results from the previous tests. To start, we characterize the optimal policy when employees constitute a single group, demonstrating some interesting trade-offs. Subsequently, we address the multi-group setting, demonstrating that when the noise levels vary across groups, a fundamental impossibility emerges whereby we cannot administer the same number of tests, subject candidates to the same decision rule, and yet realize the same outcomes in both groups. We show that by subjecting members of noisier groups to more tests, we can equalize the confusion matrix entries across groups, seemingly eliminating any disparate impact concerning outcomes.

BibTeX - Entry

@InProceedings{cohen_et_al:LIPIcs:2020:12017,
  author =	{Lee Cohen and Zachary C. Lipton and Yishay Mansour},
  title =	{{Efficient Candidate Screening Under Multiple Tests and Implications for Fairness}},
  booktitle =	{1st Symposium on Foundations of Responsible Computing (FORC 2020)},
  pages =	{1:1--1:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-142-9},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{156},
  editor =	{Aaron Roth},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/12017},
  URN =		{urn:nbn:de:0030-drops-120179},
  doi =		{10.4230/LIPIcs.FORC.2020.1},
  annote =	{Keywords: algorithmic fairness, random walk, inference}
}

Keywords: algorithmic fairness, random walk, inference
Collection: 1st Symposium on Foundations of Responsible Computing (FORC 2020)
Issue Date: 2020
Date of publication: 18.05.2020


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