The DOI matches its Crossref record; 29 of the 30 references it cites were found in a registry or an index.
The problem of infra-marginality in outcome tests for discrimination
Open accessAbstract
Outcome tests are a popular method for detecting bias in lending, hiring, and policing decisions. These tests operate by comparing the success rate of decisions across groups. For example, if loans made to minority applicants are observed to be repaid more often than loans made to whites, it suggests that only exceptionally qualified minorities are granted loans, indicating discrimination. Outcome tests, however, are known to suffer from the problem of infra-marginality: even absent discrimination, the repayment rates for minority and white loan recipients might differ if the two groups have different risk distributions. Thus, at least in theory, outcome tests can fail to accurately detect discrimination. We develop a new statistical test of discrimination—the threshold test—that mitigates the problem of infra-marginality by jointly estimating decision thresholds and risk distributions. Applying our test to a dataset of 4.5 million police stops in North Carolina, we find that the problem of infra-marginality is more than a theoretical possibility, and can cause the outcome test to yield misleading results in practice.
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In the citing paper
“thresholds cannot be identified by the observed data alone 7 , and so inferences are dependent on the specific functional form of the underlying Bayesian model, including the prior distributions.”
From A large-scale analysis of racial disparities in police stops across the United States (2020) · cites this paper as [7]
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In the citing paper
“the threshold test has been proposed as a more robust means for detecting discrimination 7,22 .”
From A large-scale analysis of racial disparities in police stops across the United States (2020) · cites this paper as [7]
In the citing paper
“However, since the protected attribute is frequently represented implicitly by some combination of proxy variables, the classifier still learns the discrimination reflected in training data.”
From Interventional Fairness: Causal Database Repair for Algorithmic Fairness (2019) · cites this paper as [46]
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“∗We thank Cheryl Phillips and Vignesh Ramachandran of the Stanford Computational Journalism Lab for helping to compile the North Carolina traffic stop data, and the John S. and James L. Knight Foundation for partial support of this research.”
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Metadata
- Authors
- Simoiu, C., Corbett-Davies, S., & Goel, S.
- Affiliations
- Stanford University (US)
- ORCID
- Goel, Sharad
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2017-09-01
- Volume
- 11
- Issue
- 3
- Pages
- 1193-1216
- ISSN
- 1932-6157 (ISSN-L)
- 1941-7330
- OpenAlex
- W2950220130
- Cited by
- 154
- References
- 30
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 16 · 2023: 21 · 2022: 17 · 2021: 22 · 2020: 28 · 2019: 17 · 2018: 15 · 2017: 2
- FWCI
- 38.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Annals of Applied Statistics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Stanford Bio-X
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- discrimination detection, threshold test, decision threshold, risk allocation, algorithmic fairness, police stops, racial disparities, loan repayment rates, hiring discrimination, credit discrimination
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