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MIT AI Risk Repository · Risk Category · 59.10.00

Harming users’ data privacy

Open the MIT Risk Navigator All entries from Schnitzer2024

Description

"Modern AI systems rely on large amounts of data. If this includes personal data about individuals, the risk of harming the privacy of persons arises."

From AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks (Schnitzer2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Causal entity
Other
Intent
Other
Timing
Other

Subdomain definition: AI systems that memorize and leak sensitive personal data or infer private information about individuals without their consent. Unexpected or unauthorized sharing of data and information can compromise user expectation of privacy, assist identity theft, or loss of confidential intellectual property.

Real-world incidents in this subdomain

Browse all incidents in this subdomain

How other frameworks describe this risk

  • Risks to privacy

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

  • Risks to privacy

    International AI Safety Report 2025 (Bengio2025)

  • Privacy Leakage

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

  • Private Training Data

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

  • Memorization in LLMs

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

  • Association in LLMs

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

  • Privacy Leakage

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

  • Privacy and regulation violations

    Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

Other entries from Schnitzer2024