Audit Studies: Behind the Scenes with Theory, Method, and Nuance
Published version of a preprint
Integrity signals
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Across all bias audit reports analyzed, employers omitted information for 20% of impact ratios for the reason that the subgroup represented less than 2% of the data.
“Across all bias audit reports analyzed, employers omitted information for 20% of impact ratios for this reason [24].”
From Null Compliance: NYC Local Law 144 and the challenges of algorithm accountability (2024) · cites this paper as [24]
Cited by
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Comparative Perspectives on Racial Discrimination in Hiring: The Rise of Field Experiments
Quillian, L., & Midtbøen, A. H. (2021) · Annual Review of Sociology · doi:10.1146/annurev-soc-090420-035144 · Cited by 180 · see citing works
The Past, Present, and Future of Experimental Methods in the Social Sciences
Mize, T. D., & Manago, B. (2022) · Social Science Research · doi:10.1016/j.ssresearch.2022.102799 · Cited by 76 · see citing works
Null Compliance: NYC Local Law 144 and the challenges of algorithm accountability
Wright, L., Muenster, R. M., Vecchione, B., Qu, T., Cai, P. (., Smith, A., Comm 2450 Student Investigators, Metcalf, J., & Matias, J. N. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658998 · Cited by 43 · see citing works
Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability
Wright, L., Muenster, R. M., Vecchione, B., Qu, T., Pika, Cai, Investigators, C. 2. S., Metcalf, J., & Matias, J. N. (2024) · arXiv · doi:10.48550/arxiv.2406.01399 · Cited by 43
Getting a Foot in the Door: A Meta-Analysis of U.S. Audit Studies of Gender Bias in Hiring
Park, S. Y., & Oh, E. (2025) · Sociological Science · doi:10.15195/v12.a2 · Cited by 7 · see citing works
- Venue
- Springer eBooks
- Publisher
- Springer International Publishing
- Published
- 2018
- OpenAlex
- W3210333953
- Cited by
- 178
- References
- 0
- Citations by year
- 2026: 3 · 2025: 20 · 2024: 24 · 2023: 20 · 2022: 25 · 2021: 30 · 2020: 19 · 2019: 17 · 2018: 17 · 2017: 3
- FWCI
- 5.17
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Keywords
- audit studies, racial discrimination, racial bias in hiring, employment discrimination
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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