Actionable Auditing Revisited: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products
Abstract
Although algorithmic auditing has emerged as a key strategy to expose systematic biases embedded in software platforms, we struggle to understand the real-world impact of these audits and continue to find it difficult to translate such independent assessments into meaningful corporate accountability. To analyze the impact of publicly naming and disclosing performance results of biased AI systems, we investigate the commercial impact of Gender Shades, the first algorithmic audit of gender- and skin-type performance disparities in commercial facial analysis models. This paper (1) outlines the audit design and structured disclosure procedure used in the Gender Shades study, (2) presents new performance metrics from targeted companies such as IBM, Microsoft, and Megvii (Face++) on the Pilot Parliaments Benchmark (PPB) as of August 2018, (3) provides performance results on PPB by non-target companies such as Amazon and Kairos, and (4) explores differences in company responses as shared through corporate communications that contextualize differences in performance on PPB. Within 7 months of the original audit, we find that all three targets released new application program interface (API) versions. All targets reduced accuracy disparities between males and females and darker- and lighter-skinned subgroups, with the most significant update occurring for the darker-skinned female subgroup that underwent a 17.7--30.4% reduction in error between audit periods. Minimizing these disparities led to a 5.72--8.3% reduction in overall error on the Pilot Parliaments Benchmark (PPB) for target corporation APIs. The overall performance of non-targets Amazon and Kairos lags significantly behind that of the targets, with error rates of 8.66% and 6.60% overall, and error rates of 31.37% and 22.50% for the darker female subgroup, respectively. This is an expanded version of an earlier publication of these results, revised for a more general audience, and updated to include commentary on further developments.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Audits have limited impact if their results are not reliable or meaningfully connected to real world requirements.
“Audits have limited impact if their results are not reliable or meaningfully connected to real world requirements [10].”
From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372873 · Cited by 1,372 · see citing works
Self-citationFace Recognition Performance: Role of Demographic Information
Klare, B. F., Burge, M. J., Klontz, J. C., Vorder Bruegge, R. W., & Jain, A. K. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2214212 · Cited by 516 · see citing works
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · arXiv · doi:10.48550/arxiv.2001.00973 · Cited by 179 · see citing works
Self-citationQuantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media
Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., & Karahalios, K. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998321 · Cited by 148 · see citing works
Flagged first, then by citations
Research works printed but not linked: 11
- O'Toole, A.J. An other-race effect for face recognition algorithms. In ACM Transactions on Applied Perception (TAP). Volume 8. ACM Press, 2011.
- Merler, M., Ratha, N., Feris, R.S., Smith, J.R. Diversity in faces. arXiv preprint arXiv:1901.10436, 2019. · arXiv
- Paullada, A., Raji, I.D., Bender, E.M., Denton, E., Hanna, A. Data and its (dis) contents: A survey of dataset development and use in machine learning research. arXiv preprint arXiv:2012.05345, 2020. · arXiv
- Raji, I.D., Buolamwini, J. Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019, 429-435.
- Raji, I.D., Fried, G. About face: A survey of facial recognition evaluation. arXiv preprint arXiv:2102.00813, 2021. · arXiv
- Raji, I.D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., Denton, E. Saving face: Investigating the ethical concerns of facial recognition auditing. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2020, 145-151.
- Buolamwini, J., Gebru, T. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of Machine Learning Research. Conference on Fairness, Accountability, and Transparency, February 2018.
- Christian Sandvig, K.K., Hamilton, K., Langbort, C. Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and Discrimination, Converting Critical Concerns into Productive: A Pre-conference at the 64 th Annual Meeting of the International Communication Association, 2014.
- Crenshaw, K. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum, 1989(8), 1989.
- Edelman, B., Luca, M. Digital discrimination: The case of airbnb. com. SSRN Electron. J 2014.
- Irene Chen and D. Sontag. Why is my classifier discriminatory? In arXiv preprint. arXiv, 2018.
Cited by
Sorted by citations
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Ojewale, V., Steed, R., Vecchione, B., Birhane, A., & Raji, I. D. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713301 · Cited by 81 · see citing works
Self-citationIgbinovia, M. O., & Mensah Danquah, M. (2025) · Technical Services Quarterly · doi:10.1080/07317131.2025.2512282 · Cited by 22 · see citing works
- Authors
- Raji, I. D., & Buolamwini, J.
- Affiliations
- University of Toronto, Canada
- Massachusetts Institute of Technology, Cambridge, MA
- Venue
- Communications of the ACM
- Published
- 2022-12-20
- Volume
- 66
- Issue
- 1
- Pages
- 101-108
- ISSN
- 0001-0782
- 1557-7317
- Cited by
- 38
- References
- 4
- Language
- en
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review