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

167

Report Count

Incident Date

2017-09-07

Editors

Sean McGregor, Khoa Lam

Applied Taxonomies

GMF

GMF Taxonomy Classifications

Taxonomy Details

Known AI Goal Snippets

(Snippet Text: Presented with photos of gay men and straight men, a computer program was able to determine which of the two was gay with 81 percent accuracy, according to Dr. Kosinski and co-author Yilun Wang’s paper., Related Classifications: Behavioral Modeling, Snippet Discussion: Pairwise classification)

Incident Reports

Reports Timeline

Why Stanford Researchers Tried to Create a ‘Gaydar’ Machine

nytimes.com

nytimes.com · 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…

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.