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.
Entities
View all entitiesAlleged: 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
167
Report Count
1
Incident Date
2017-09-07
Editors
Sean McGregor, Khoa Lam
GMF Taxonomy Classifications
Taxonomy DetailsKnown 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
Reports Timeline
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…
Variants
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.
