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.

Tools

New ReportNew ReportNew ResponseNew ResponseDiscoverDiscoverView HistoryView History

Entities

View all entities
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
1
Incident Date
2017-09-07
Editors
Sean McGregor, Khoa Lam
Applied Taxonomies
GMF, MIT

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)

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

2.1. Compromise of privacy by obtaining, leaking or correctly inferring sensitive information

Risk Domain

  1. Privacy & Security

Entity

Human

Timing

Post-deployment

Intent

Intentional

Incident Reports

Reports Timeline

Incident OccurrenceWhy Stanford Researchers Tried to Create a ‘Gaydar’ Machine
Why Stanford Researchers Tried to Create a ‘Gaydar’ Machine
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.

Similar Incidents

By textual similarity

Did our AI mess up? Flag the unrelated incidents

Research Prototype AI, Delphi, Reportedly Gave Racially Biased Answers on Ethics

Research Prototype AI, Delphi, Reportedly Gave Racially Biased Answers on Ethics

· 3 reports
Fake LinkedIn Profiles Created Using GAN Photos

Fake LinkedIn Profiles Created Using GAN Photos

· 4 reports
Ever AI Reportedly Deceived Customers about FRT Use in App

Ever AI Reportedly Deceived Customers about FRT Use in App

· 7 reports
Previous IncidentNext Incident