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MIT AI Risk Repository · Risk Sub-Category · 62.38.01

Decision-making on inferred private data

Category: Impacts of AI (Privacy)

Open the MIT Risk Navigator All entries from Gipiškis2024

Description

"Current GPAIs (LLMs and multimodal LLM-based models) have significant capability to infer correlations in text data. In some cases, they may be able to make highly accurate data inferences on users based on contextual input that users provide [134]. These data inferences can “leak” or reveal sensitive information about the user, cause unfair treatment, or enable manipulation of user behavior."

From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Causal entity
AI
Intent
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 Gipiškis2024