Physiognomic Artificial Intelligence
Abstract
The reanimation of the pseudosciences of physiognomy and phrenology at scale through computer vision and machine learning is a matter of urgent concern. This Article, which contributes to critical data studies, consumer protection law, biometric privacy law, and anti-discrimination law, endeavors to conceptualize and problematize physiognomic artificial intelligence (AI) and offer policy recommendations for state and federal lawmakers to forestall its proliferation. Physiognomic AI, we contend, is the practice of using computer software and related systems to infer or create hierarchies of an individual’s body composition, protected class status, perceived character, capabilities, and future social outcomes based on their physical or behavioral characteristics. Physiognomic and phrenological logics are intrinsic to the technical mechanism of computer vision applied to humans. In this Article, we observe how computer vision is a central vector for physiognomic AI technologies, unpacking how computer vision reanimates physiognomy in conception, form, and practice and the dangers this trend presents for civil liberties. This Article thus argues for legislative action to forestall and roll back the proliferation of physiognomic AI. To that end, we consider a potential menu of safeguards and limitations to significantly limit the deployment of physiognomic AI systems, which we hope can be used to strengthen local, state, and federal legislation. We foreground our policy discussion by proposing the abolition of physiognomic AI. From there, we posit regimes of U.S. consumer protection law, biometric privacy law, and civil rights law as vehicles for rejecting physiognomy’s digital renaissance in artificial intelligence. Specifically, we argue that physiognomic AI should be categorically rejected as oppressive and unjust. Second, we argue that lawmakers should declare physiognomic AI to be unfair and deceptive per se. Third, we argue that lawmakers should enact or expand biometric privacy laws to prohibit physiognomic AI. Fourth, we argue that lawmakers should prohibit physiognomic AI in places of public accommodation. We also observe the paucity of procedural and managerial regimes of fairness, accountability, and transparency in addressing physiognomic AI and attend to potential counterarguments in support of physiognomic AI.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Interpretability and explainability methods promoted by popular tools vary widely in their goals and may encourage false confidence in their users.
“Interpretability and explainability methods promoted by popular tools vary widely in their goals [120] and, like many 'snake oil' AI products [18, 59, 91], may encourage false confidence in their users [118].”
From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [91]
Cites
Barrett, L. F. (2006) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2006.00003.x · Cited by 1,859 · see citing works
Jacobs, A. Z., & Wallach, H. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445901 · Cited by 362 · see citing works
Cheating-Detection Companies Made Millions During the Pandemic. Now Students Are Fighting back *
Harwell, D. (2022) · Ethics of Data and Analytics · doi:10.1201/9781003278290-60 · Cited by 22 · see citing works
The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
Goldenfein, J. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287568 · Cited by 21 · see citing works
Flagged first, then by citations
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
Zheng, W., Yu, J., Xia, R., & Wang, S. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.861 · Cited by 51 · see citing works
On manipulation by emotional AI: UK adults’ views and governance implications
Bakir, V., Laffer, A., McStay, A., Miranda, D., & Urquhart, L. (2024) · Frontiers in Sociology · doi:10.3389/fsoc.2024.1339834 · Cited by 34 · see citing works
The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models
Birhane, A., Dehdashtian, S., Prabhu, V., & Boddeti, V. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658968 · Cited by 28 · see citing works
- Authors
- Stark, L., & Hutson, J.
- ORCID
- Stark, Luke
- Publisher
- Elsevier BV
- Published
- 2021
- Cited by
- 40
- References
- 4
- Type
- Preprint
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