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MIT AI Risk Repository · Risk Sub-Category · 33.01.02

Bias

Category: Ethical Concerns

Open the MIT Risk Navigator All entries from Nah2023

Description

"In the context of AI, the concept of bias refers to the inclination that AIgenerated responses or recommendations could be unfairly favoring or against one person or group (Ntoutsi et al., 2020). Biases of different forms are sometimes observed in the content generated by language models, which could be an outcome of the training data. For example, exclusionary norms occur when the training data represents only a fraction of the population (Zhuo et al., 2023). Similarly, monolingual bias in multilingualism arises when the training data is in one single language (Weidinger et al., 2021). As Ch

From Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration (Nah2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Causal entity
AI
Timing
Other

Subdomain definition: Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and representation of those groups.

Real-world incidents in this subdomain

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How other frameworks describe this risk

  • Discrimination

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

  • Harms of Representation and Other Biases

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

  • Risks from bias and underrepresentation

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

  • Bias

    International AI Safety Report 2025 (Bengio2025)

  • Bias

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

  • Toxicity and Bias Tendencies

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

  • Biased Training Data

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

  • Broken systems

    Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

Other entries from Nah2023