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Incident 579: Harmful Stereotyping of Non-Cisgendered People via Text-to-Image Systems

Description: Text-to-image systems such as DALL-E are allegedly generating biased and often insulting representations of non-cisgender identities. The systems tend to generate stereotypical and sexualized images when prompted with gender identity terms like "trans," "nonbinary," or "queer," highlighting systemic issues of bias.

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Alleged: OpenAI developed an AI system deployed by DALL-E, which harmed Non-cisgender individuals and LGBTQ+ community.

Incident Stats

Incident ID
579
Report Count
1
Incident Date
2023-07-03
Editors
Applied Taxonomies
MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

1.1. Unfair discrimination and misrepresentation

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. Discrimination and Toxicity

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

+1
Don’t ask DALL-E to Draw Trans People
Don’t ask DALL-E to Draw Trans People

Don’t ask DALL-E to Draw Trans People

queerinai.com

Don’t ask DALL-E to Draw Trans People
queerinai.com · 2023

One thing that I love about attending Queer in AI events (or queer community gatherings in general) is that I can assume that everyone around me is queer, too. I shift into a more comfortable, less guarded state. And conversely, it feels go…

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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