The DOI matches its Crossref record; 53 of the 66 references it cites were found in a registry or an index.
Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.
Open accessPublished version of a preprint
Abstract
We show that faces contain much more information about sexual orientation than can be perceived or interpreted by the human brain. We used deep neural networks to extract features from 35,326 facial images. These features were entered into a logistic regression aimed at classifying sexual orientation. Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 81% of cases, and in 71% of cases for women. Human judges achieved much lower accuracy: 61% for men and 54% for women. The accuracy of the algorithm increased to 91% and 83%, respectively, given five facial images per person. Facial features employed by the classifier included both fixed (e.g., nose shape) and transient facial features (e.g., grooming style). Consistent with the prenatal hormone theory of sexual orientation, gay men and women tended to have gender-atypical facial morphology, expression, and grooming styles. Prediction models aimed at gender alone allowed for detecting gay males with 57% accuracy and gay females with 58% accuracy. Those findings advance our understanding of the origins of sexual orientation and the limits of human perception. Additionally, given that companies and governments are increasingly using computer vision algorithms to detect people's intimate traits, our findings expose a threat to the privacy and safety of gay men and women. (PsycINFO Database Record
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“For example, a recent paper used publicly available images of faces uploaded to a dating website as a way to train AI software to detect someone’s sexuality based on a small number of photos (Wang and Kosinski 2018). While the study received ethics committee approval, it raises further questions around consent, since it is implausible that the users of the dating website could or necessarily would”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Wang and Kosinski 2018
In the citing paper
“For example, a study found that computer vision algorithms were used to ascertain sexual orientation of people by scrutinizing thousands of facial images taken from public profiles on a dating website (Wang and Kosinski, 2017).”
From SHIFTing artificial intelligence to be responsible in healthcare: a systematic review (2022) · cites this paper as Wang and Kosinski, 2017
Citations
Cites
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Dermatologist-level classification of skin cancer with deep neural networks
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Private traits and attributes are predictable from digital records of human behavior
Kosinski, M., Stillwell, D., & Graepel, T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218772110 · Cited by 2,829
Self-citationThe Evolutionary Psychology of Facial Beauty
Rhodes, G. (2005) · Annual Review of Psychology · doi:10.1146/annurev.psych.57.102904.190208 · Cited by 1,821
Inferences of competence from faces predict election outcomes
Todorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005) · Science · doi:10.1126/science.1110589 · Cited by 1,508
Social Cognition: Thinking Categorically about Others
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How artificial intelligence will change the future of marketing
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00696-0 · Cited by 2,875
“For example, two Stanford researchers used deep neural networks to identify people ’ s sexual orientation, merely by analyzing facial images (Wang and Kosinski 2018).”
BackgroundThe Ethics of AI Ethics -- An Evaluation of Guidelines
Hagendorff, T. (2019) · arXiv · doi:10.48550/arxiv.1903.03425 · Cited by 1,907
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911
Taxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764
“Such systems may never be accurate - for example, image classification models that attempt to infer unobservable characteristics, such as sexual orientation from a portrait [190], are inherently prone to error.”
BackgroundTowards a standard for identifying and managing bias in artificial intelligence
Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022) · doi:10.6028/nist.sp.1270 · Cited by 548
Metadata
- Authors
- Wang, Y., & Kosinski, M.
- Affiliations
- Stanford University (US)
- ORCID
- Kosinski, Michal
- Venue
- Journal of Personality and Social Psychology
- Published
- 2018-02
- Volume
- 114
- Issue
- 2
- Pages
- 246-257
- ISSN
- 0022-3514 (ISSN-L)
- 1939-1315
- OpenAlex
- W2781265533
- Cited by
- 766
- References
- 66
- Citations by year
- 2026: 29 · 2025: 57 · 2024: 57 · 2023: 146 · 2022: 98 · 2021: 92 · 2020: 155 · 2019: 75 · 2018: 51
- FWCI
- 55.54
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- Journal of Personality and Social Psychology (publishedVersion)
- PubMed (publishedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Evolutionary Psychology and Human Behavior
- Keywords
- face images, deep neural network, gay men, gay women, facial morphology, privacy, computer vision, logistic regression
- MeSH terms
- Facial Recognition, Deep Learning, Adult, Face, Female, Humans, Male, Social Perception, Sexuality
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