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MIT AI Risk Repository · Risk Sub-Category · 20.02.04

AI discrimination

Category: AI Ethics

Open the MIT Risk Navigator All entries from Wirtz2020

Description

"AI discrimination is a challenge raised by many researchers and governments and refers to the prevention of bias and injustice caused by the actions of AI systems (Bostrom & Yudkowsky, 2014; Weyerer & Langer, 2019). If the dataset used to train an algorithm does not reflect the real world accurately, the AI could learn false associations or prejudices and will carry those into its future data processing. If an AI algorithm is used to compute information relevant to human decisions, such as hiring or applying for a loan or mortgage, biased data can lead to discrimination against parts of the s

From The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration (Wirtz2020), 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

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)

  • Biased Training Data

    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)

  • Bias

    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 Wirtz2020