Incident 59: Gender Biases in Google Translate

Description: A Cornell University study in 2016 highlighted Google Translate's pattern of assigning gender to occupations in a way showing an implicit gender bias against women.

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Alleged: Google developed and deployed an AI system, which harmed Women.

Incident Stats

Incident ID
59
Report Count
10
Incident Date
2017-04-13
Editors
Sean McGregor

CSET Taxonomy Classifications

Taxonomy Details

Full Description

A Cornell University study in 2016 highlighted Google Translate's pattern of assigning gender to occupations in a way showing an implicit gender bias against women. When translating from non-gendered languages (ex. Turkish, Finnish), Google Translate added gender to the phrases being translated. "Historian" "Doctor" "President" "Engineer" and "Soldier" were assigned male gender pronouns while "Nurse" "Teacher" and "Shop Assistant" were assigned female gender pronouns.

Short Description

A Cornell University study in 2016 highlighted Google Translate's pattern of assigning gender to occupations in a way showing an implicit gender bias against women.

Severity

Negligible

Harm Distribution Basis

Sex

Harm Type

Harm to social or political systems

AI System Description

Google Translate, a software allowing for translations between many languages

System Developer

Google

Sector of Deployment

Information and communication

Relevant AI functions

Perception, Cognition, Action

AI Techniques

Google Translate

AI Applications

language API, language translation

Named Entities

Google Translate, Google

Technology Purveyor

Google

Beginning Date

2016-01-01T00:00:00.000Z

Ending Date

2016-01-01T00:00:00.000Z

Near Miss

Harm caused

Intent

Unclear

Lives Lost

No

Data Inputs

User entered translation requests

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.

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By textual similarity

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