Incident 167: Researchers' Homosexual-Men Detection Model Denounced as a Threat to LGBTQ People’s Safety and Privacy

Description: Researchers at Stanford Graduate School of Business developed a model that determined, on a binary scale, whether someone was homosexual using only his facial image, which advocacy groups such as GLAAD and the Human Rights Campaign denounced as flawed science and threatening to LGBTQ folks.


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Alleged: Michal Kosinski and Yilun Wang developed and deployed an AI system, which harmed LGBTQ people , LGBTQ people of color and non-American LGBTQ people.

Incident Stats

Incident ID
Report Count
Incident Date
Sean McGregor, Khoa Lam

GMF Taxonomy Classifications

Taxonomy Details

Known AI Goal

Behavioral Modeling

Known AI Technology

Neural Network

Potential AI Technology

Siamese Network, Convolutional Neural Network, Diverse Data

Known AI Technical Failure

Limited Dataset, Dataset Imbalance, Generalization Failure

Potential AI Technical Failure

Incomplete Data Attribute Capture, Overfitting, Lack of Explainability

Incident Reports

Why Stanford Researchers Tried to Create a ‘Gaydar’ Machine · 2017

Michal Kosinski felt he had good reason to teach a machine to detect sexual orientation.

An Israeli start-up had started hawking a service that predicted terrorist proclivities based on facial analysis. Chinese companies were developing fac…


A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.