PromptRiskDBThreat intelligence atlas

Promoting harmful stereotypes by implying gender or ethnic identity

AI Risk

"A conversational agent may invoke associations that perpetuate harmful stereotypes, either by using particular identity markers in language (e.g. referring to “self” as “female”), or by more general design features (e.g. by giving the product a gendered name)."

Overview

A source-backed snapshot of this AI risk.

Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Records2Source records unified into this concept.

Risk profile

How the MIT AI Risk Repository categorizes this risk.

Domain1. Discrimination & Toxicity
Subdomain1.1 > Unfair discrimination and misrepresentation
Entity2 - AI
Intent2 - Unintentional
Timing2 - Post-deployment
CategoryHuman-Computer Interaction Harms; Risk area 5: Human-Computer Interaction Harms
SubcategoryPromoting harmful stereotypes by implying gender or ethnic identity

Merged risk records

Source records unified into this canonical risk concept.

2 recordsView all →

MITRISK-Weidinger2021-17.05.03 - Promoting harmful stereotypes by implying gender or ethnic identity

"A conversational agent may invoke associations that perpetuate harmful stereotypes, either by using particular identity markers in language (e.g. referring to “self” as “female”), or by more general design features (e.g. by giving the product a gendered name)."

Domain1. Discrimination & ToxicitySubdomain1.1 > Unfair discrimination and misrepresentationSourceEthical and social risks of harm from language modelsYear2021

MITRISK-Weidinger2022-16.05.01 - Promoting harmful stereotypes by implying gender or ethnic identity

"CAs can perpetuate harmful stereotypes by using particular identity markers in language (e.g. referring to “self” as “female”), or by more general design features (e.g. by giving the product a gendered name such as Alexa). The risk of representational harm in these cases is that the role of “assistant” is presented as inherently linked to the female gender [19, 36]. Gender or ethnicity identity markers may be implied by CA vocabulary, knowledge or vernacular [124]; product description, e.g. in one case where users could choose as virtual assistant Jake - White, Darnell - Black, Antonio - Hispanic [117]; or the CA’s explicit self-description during dialogue with the user."

Domain1. Discrimination & ToxicitySubdomain1.1 > Unfair discrimination and misrepresentationSourceTaxonomy of Risks posed by Language ModelsYear2022

Mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source evidence

Research source for this risk, when available.