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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479
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
Macrae, C. N., & Bodenhausen, G. V. (2000) · Annual Review of Psychology · doi:10.1146/annurev.psych.51.1.93 · Cited by 1,432
The evolution of human skin coloration
Jablonski, N. G., & Chaplin, G. (2000) · Journal of Human Evolution · doi:10.1006/jhev.2000.0403 · Cited by 1,212
Kosiński, M., Matz, S., Gosling, S. D., Popov, V., & Stillwell, D. J. (2015) · American Psychologist · doi:10.1037/a0039210 · Cited by 1,031
Self-citation
Cited by
Sorted by citations
Items 601–700 of 778
Alexander, D. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108566520.017
Oberprantacher, A. (2018) · Reihe Interkulturelle Philosophie · doi:10.1007/978-3-658-22827-9_21
Eaton, M. (2020) · Springer eBooks · doi:10.1007/978-3-030-55300-5_1
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0009
Elias, C. (2020) · doi:10.1002/9781119415480.ch25
Jemielniak, D. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198839705.003.0001
Jemielniak, D. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198839705.003.0005
Alexander, D. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108566520.014
(2013) · Routledge eBooks · doi:10.4324/9781315827629-15
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.010
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0001
Predicting Political Inclinations using Profile Pictures: Do Self-reported Covariates Matter?Preprint
Singh, B., & Tulabandhula, T. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3915259
Kovač, M. (2020) · Springer eBooks · doi:10.1007/978-3-030-53644-2_1
Jemielniak, D. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198839705.003.0002
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0006
Hartmann, K. V. (2022) · V&R unipress eBooks · doi:10.14220/9783737014793.149
Obstacles and Lessons Implementing a Differential Privacy-Based Open Data ProgramPreprint
Barbagallo, L., & Roccatagliata, P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4147840
A Perspective on Fairness in Artificial IntelligencePreprint
Bhattacharya, P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4116921
Training to deal with Otherness – rehearsing & maintaining human-machine relations
Scherffig, L., Muxel, A., Faubel, C., et al. (2021) · Electronic workshops in computing · doi:10.14236/ewic/pom2021.48
Höfling, M. D. S. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.2.2022.tde-16092022-121920
Una genealogía queer de los algoritmos computacionales.
Martín, F. R. S. (2022) · doi:10.4995/thesis/10251/187463
Les dimensions éthiques et sociétales de l’adoption des technologies émergentes
Lejealle, C. (2022) · Recherche et Cas en Sciences de Gestion · doi:10.3917/rcsg.022.0031
Determination of Landmarks on Vietnamese Human Face Using Convolutional Neural Network
Hai, N. D. X., & Thinh, N. T. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-19694-2_54
Musil, B., & Plohl, N. (2022) · doi:10.18690/um.ff.11.2022.11 · Cited by 1
Umetna inteligenca in eksistenčno tveganje
Lipuš, A. (2022) · doi:10.18690/um.ff.11.2022.10
Borstner, B., & Šetar, N. (2022) · doi:10.18690/um.ff.11.2022.5
Transformacija 'inteligence roja' {Swarm Intelligence} na umetno inteligenco
Martinc, U., Aberšek, B., & Borstner, B. (2022) · doi:10.18690/um.ff.11.2022.9
(2022) · transcript Verlag eBooks · doi:10.1515/9783839461891
AI & Human Ranking It Is Here, Can We Live with It?Preprint
Greenbaum, D. S., Cousens, D. E., Habas, D., & Baran, O. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4194107
Unpacking the CNN’s Algorithmic “Glass Ceiling” of Inferring Psychological Traits from Facial Images
Li, B., & Chen, H. (2022) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc57393.2022.9995474
“Wang et al. [1] used DNN to extract features from facial images on social platforms to predict their sexual orientation.”
BackgroundStrengthening the Civic Body as the Bandwidth for Optimised Emotion Expands
Bakir, V., & McStay, A. (2022) · Springer eBooks · doi:10.1007/978-3-031-13551-4_9
Von Gesichtsweiblichkeit und Verweiblichung
Hartmann, K. V. (2022) · AI critique · doi:10.14361/9783839461891-004
Bruss, S. M. D. S. (2022) · AI critique · doi:10.14361/9783839461891-014
Twardoch-Raś, E. (2022) · Przegląd Kulturoznawczy · doi:10.4467/20843860pk.22.026.16615
Çakmakçı, C., Magalhães, D. R., Pacor, V. R., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4478400
Martín, F. R. S. (2023) · Afterimage · doi:10.1525/aft.2023.50.2.112
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902-005
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902-007
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902-006
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902-001
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902-toc
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902-fm
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902-003
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902-002
Althoff, S. (2023) · Digitale Gesellschaft · doi:10.14361/9783839466902-004
Althoff, S. (2023) · transcript Verlag eBooks · doi:10.1515/9783839466902-007
The power of computer vision : a critital analysis
Waelen, R. A. (2023) · doi:10.3990/1.9789036557948
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0009
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0001
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0004
Mapping the Ethics of Algorithms
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0007
How to Deliver a Good AI Society
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0010
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0007
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0008
List of Most Common Abbreviations and Acronyms
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0011
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0011
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0010
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0009
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0005
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0013
NEURAL NETWORKS IN JUSTICE: STATE OF THE ART AND PROSPECTS
Дзядевич, В. (2023) · Juridical scientific and electronic journal · doi:10.32782/2524-0374/2023-7/108
“Разом із тим користуючись значною вибіркою, сучасні штучні інтелекти можуть з великою вірогідністю визначати схильність до політичних переконань [12], лише за фотографією обличчя більш ніж із 89% успіхом визначати схильність до вчинення правопорушень [13] або навіть визначати сексуальну орієнтацію із точністю 81% у чо…”
[RETRACTED: The Ethical Implications of Artificial Intelligence on the Human Society]Preprint
Ansari, W. (2023) · doi:10.14293/pr2199.000341.v1 · Cited by 1
From Pixels to Power: Critical Feminist Questions for the Ethics of Computer VisionPreprint
Oswald, F. (2023) · doi:10.31234/osf.io/bsp39
Konstantinov, V. V., Ustin, P. N., & Popov, L. M. (2023) · OBM Integrative and Complementary Medicine · doi:10.21926/obm.icm.2304049
Estudios contemporáneos de derecho privado
Estrada, O. L. T., Quesada, Á. D. M., Gaviria, J. A., et al. (2023) · Ediciones Unibagué eBooks · doi:10.35707/9789587544183
Shevcov, A., Ustin, P. N., Popov, L. M., Gafarov, F. M., & Vakhitov, G. (2023) · OBM Neurobiology · doi:10.21926/obm.neurobiol.2304195
Belavadi, V., Zhou, Y., Kantarcıoğlu, M., et al. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623177
“For example, an ML classifier may be trained to predict privacy-sensitive information such as sexual orientation (e.g.,[27, 33]), political affiliation (e.g., [13, 33]), attractiveness [10, 15], and life satisfaction [2, 28] using social network profiles.”
Do Fictive Sexual Orientations Induce In-Group Bias in Emotion Recognition?
Bucher, B., Kobayashi, M., & Watanabe, K. (2023) · International Computer Science and Engineering Conference · doi:10.1109/icsec59635.2023.10329647
“Interestingly, both machine learning and humans seem to primarily use gender atypicality to recognize sexual minorities, based on facial features (such as the shape of the jawline) or on grooming style [1,2].”
BackgroundThe Moral Significance of Privacy Dependencies
Munch, L. A., & Mainz, J. T. (2023) · Philosophy & Technology · doi:10.1007/s13347-023-00680-1
“For instance, a class of privacy dependencies have come to our attention thanks to research suggesting that state-of–the-art algorithmic systems relying upon statistical techniques and fueled by Big Data have proven capable of inferring sensitive personal information from mundane sources.”
Appel, M., & Weber, S. (2023) · Springer eBooks · doi:10.1007/978-3-662-66608-1_5
Grounded theory in AI-enhanced women’s image in advertising
Kriaučiūnaitė-Lazauskienė, G. (2023) · Media & Marketing Identity · doi:10.34135/mmidentity-2023-23 · Cited by 1
A Perspective on Data Categorization with Regard to Equity, Diversity, and Inclusion in Data Science
Favre, C., & Vialaret, M. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386322
“It is imperative to question and critically assess the motivations and implications of the research they undertake (as demonstrated earlier with the example of [3]).”
BackgroundUnternährer, M. (2024) · Digitale Soziologie · doi:10.1515/9783839470596-013
The Collective Constitution of Public Persona
Marshall, P. D., Moore, C., & Barbour, K. (2019) · Persona Studies · doi:10.1002/9781394261185.ch4
The Mismeasure of Music “On Computerized Music Listening and Analysis via Machine Learning”
Sturm, B. L. T., & Wiggins, G. A. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190945442.013.33
Ethical disagreements in technology and management research
Telkamp, J. B. (2023) · doi:10.31274/td-20240329-291
Rajashree, R. H., Sundarakantham, K., & Shalinie, S. M. (2023) · Signals and communication technology · doi:10.1007/978-3-031-47942-7_1
Nemitz, P. (2020) · doi:10.1093/oso/9780198847977.002.0005
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0012
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0009
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0010
Kuner, C., & Greenleaf, G. (2020) · doi:10.1093/oso/9780198847977.002.0006
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0004
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0011
Jemielniak, D. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198839705.002.0004
Jemielniak, D. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198839705.002.0007
Jemielniak, D. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198839705.002.0006
Jemielniak, D. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198839705.002.0003
Artificial Intelligence and the Right to Housing
Compton, C., & Hohmann, J. M. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0024
Robot Rights/Human Responsibility
Gunkel, D. J. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0031
Artificial Intelligence Technologies and the Right to Seek and Enjoy Asylum
Raimy, R. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0021 · Cited by 1
Artificial Intelligence and the Right to Food
Omotubora, A. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0023
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
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
