Access to population-level signaling as a source of inequality

Nicole Immorlica, Katrina Ligett, Juba Ziani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

We identify and explore differential access to population-level signaling (also known as information design) as a source of unequal access to opportunity. A population-level signaler has potentially noisy observations of a binary type for each member of a population and, based on this, produces a signal about each member. A decision-maker infers types from signals and accepts those individuals whose type is high in expectation. We assume the signaler of the disadvantaged population reveals her observations to the decision-maker, whereas the signaler of the advantaged population forms signals strategically. We study the expected utility of the populations as measured by the fraction of accepted members, as well as the false positive rates (FPR) and false negative rates (FNR). We first show the intuitive results that for a fixed environment, the advantaged population has higher expected utility, higher FPR, and lower FNR, than the disadvantaged one (despite having identical population quality), and that more accurate observations improve the expected utility of the advantaged population while harming that of the disadvantaged one. We next explore the introduction of a publicly-observable signal, such as a test score, as a potential intervention. Our main finding is that this natural intervention, intended to reduce the inequality between the populations' utilities, may actually exacerbate it in settings where observations and test scores are noisy.

Original languageAmerican English
Title of host publicationFAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency
PublisherAssociation for Computing Machinery, Inc
Pages249-258
Number of pages10
ISBN (Electronic)9781450361255
DOIs
StatePublished - 29 Jan 2019
Event2019 ACM Conference on Fairness, Accountability, and Transparency, FAT* 2019 - Atlanta, United States
Duration: 29 Jan 201931 Jan 2019

Publication series

NameFAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency

Conference

Conference2019 ACM Conference on Fairness, Accountability, and Transparency, FAT* 2019
Country/TerritoryUnited States
CityAtlanta
Period29/01/1931/01/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

  • Fairness
  • Information design
  • Strategic signaling
  • University admissions

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