AIPolicyTracker
Menu

AI incident #1000 ·

Sora Video Generator Has Reportedly Been Creating Biased Human Representations Across Race, Gender, and Disability

Open on the AI Incident Database 2 news reports Synced from the AIID API · record last edited 5 Sep 2026

What happened

A WIRED investigation found that OpenAI's video generation model, Sora, exhibits representational bias across race, gender, body type, and disability. In tests using 250 prompts, Sora was more likely to depict CEOs and professors as men, flight attendants and childcare workers as women, and showed limited or stereotypical portrayals of disabled individuals and people with larger bodies.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (2)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. OpenAI’s Sora Is Plagued by Sexist, Racist, and Ableist Biases
    wired.com · Reece Rogers, Victoria Turk · AIID #5007
  2. OpenAI's Sora Has a Small Problem With Being Hugely Racist and Sexist
    futurism.com · Frank Landymore · AIID #5029

Who was involved

Alleged deployer
OpenAI
On AIID: OpenAI
Alleged harmed party
Women and girls Women People with larger bodies People with disabilities People of color Marginalized groups LGBTQ+ people General public
On AIID: Women and girls, Women, People with larger bodies, People with disabilities, People of color, Marginalized groups, LGBTQ+ people, General public

AI systems implicated

Synthetic video generation technologySynthetic media generation technologySora

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 1.1.

  • Discrimination

    "Discrimination - Unfair or inadequate treatment or arbitrary distinction based on a person’s race, ethnicity, age, gender, sexual preference, religion, national origin, marital status, disability, language, or other pro...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Harms of Representation and Other Biases

    "A pretrained LLM generally has many of the stereotypical biases commonly present in the human society (Touvron et al., 2023). This makes it difficult for users to trust that LLMs will work well for them and not produce...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Risks from bias and underrepresentation

    "The outputs and impacts of general- purpose AI systems can be biased with respect to various aspects of human identity, including race, gender, culture, age, and disability. This creates risks in high- stakes domains su...

    International Scientific Report on the Safety of Advanced AI (Bengio2024)

  • Bias

    "General-purpose AI systems can amplify social and political biases, causing concrete harm. They frequently display biases with respect to race, gender, culture, age, disability, political opinion, or other aspects of hu...

    International AI Safety Report 2025 (Bengio2025)

  • Bias

    "The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Toxicity and Bias Tendencies

    "Extensive data collection in LLMs brings toxic content and stereotypical bias into the training data."

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Biased Training Data

    "Compared with the definition of toxicity, the definition of bias is more subjective and contextdependent. Based on previous work [97], [101], we describe the bias as disparities that could raise demographic differences...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Broken systems

    "These are the most mentioned cases. They refer to situations where the algorithm or the training data lead to unreliable outputs. These systems frequently assign disproportionate weight to some variables, like race or g...

    Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

Linked by AIID editors or by its text-similarity model.

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving OpenAI