MIT AI Risk Repository · Risk Sub-Category · 29.01.01

Bias and Discrimination

Category: AI Trust Management

Description

as they claim to generate biased and discriminatory results, these AI systems have a negative impact on the rights of individuals, principles of adjudication, and overall judicial integrity

From Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions (Habbal2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Causal entity
AI

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

Browse all incidents in this subdomain

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 Habbal2024