MIT AI Risk Repository

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39 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.

Reset Also filtered by framework NIST2024 ×

39 entries

  1. "Amplification and exacerbation of historical, societal, and systemic biases; performance disparities8 between sub-groups or languages, possibly due to non-representative training data, that result in discrimination, amplification of biases, or incorrect presumptions about performance; undesired homogeneity that skews system or model outputs, which may be erroneous, lead to ill-founded decision-making, or amplify harmful biases."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  2. "Eased production of and access to violent, inciting, radicalizing, or threatening content as well as recommendations to carry out self-harm or conduct illegal activities. Includes difficulty controlling public exposure to hateful and disparaging or stereotyping content."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  3. "Eased production of and access to obscene, degrading, and/or abusive imagery which can cause harm, including synthetic child sexual abuse material (CSAM), and nonconsensual intimate images (NCII) of adults."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  4. 48.04.00 · Risk Category

    Data Privacy

    "Impacts due to leakage and unauthorized use, disclosure, or de-anonymization of biometric, health, location, or other personally identifiable information or sensitive data."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  5. 48.02.00 · Risk Category

    Confabulation

    "The production of confidently stated but erroneous or false content (known colloquially as “hallucinations” or “fabrications”) by which users may be misled or deceived."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  6. "Lowered barrier to entry to generate and support the exchange and consumption of content which may not distinguish fact from opinion or fiction or acknowledge uncertainties, or could be leveraged for large-scale dis- and mis-information campaigns."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  7. "Eased access to or synthesis of materially nefarious information or design capabilities related to chemical, biological, radiological, or nuclear (CBRN) weapons or other dangerous materials or agents."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  8. "Lowered barriers for offensive cyber capabilities, including via automated discovery and exploitation of vulnerabilities to ease hacking, malware, phishing, offensive cyber operations, or other cyberattacks; increased attack surface for targeted cyberattacks, which may compromise a system’s availability or the confidentiality or integrity of training data, code, or model weights."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  9. "Arrangement s of or interactions between a human and an AI system which can result in the human inappropriately anthropomorphizing GAI systems or experiencing algorithmic aversion, automation bias, over-reliance, or emotional entanglement with GAI systems."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  10. "Eased production or replication of alleged copyrighted, trademarked, or licensed content without authorization (possibly in situations which do not fall under fair use); eased exposure of trade secrets; or plagiarism or illegal replication."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  11. "Impacts due to high compute resource utilization in training or operating GAI models, and related outcomes that may adversely impact ecosystems."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  12. "Non-transparent or untraceable integration of upstream third-party components, including data that has been improperly obtained or not processed and cleaned due to increased automation from GAI; improper supplier vetting across the AI lifecycle; or other issues that diminish transparency or accountability for downstream users."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  13. 48.02.00.a · Additional evidence

    Confabulation

  14. 48.02.00.b · Additional evidence

    Confabulation

  15. 48.04.00.a · Additional evidence

    Data Privacy

  16. 48.04.00.b · Additional evidence

    Data Privacy

  17. 48.05.00.a · Additional evidence

    Environmental Impacts

  18. 48.05.00.b · Additional evidence

    Environmental Impacts

  19. 48.07.00.a · Additional evidence

    Human-AI Configuration

  20. 48.07.00.b · Additional evidence

    Human-AI Configuration

  21. 48.08.00.a · Additional evidence

    Information Integrity

  22. 48.08.00.b · Additional evidence

    Information Integrity

  23. 48.08.00.c · Additional evidence

    Information Integrity

  24. 48.09.00.a · Additional evidence

    Information Security

  25. 48.09.00.b · Additional evidence

    Information Security

  26. 48.09.00.c · Additional evidence

    Information Security

  27. 48.10.00.a · Additional evidence

    Intellectual Property

  28. 48.10.00.b · Additional evidence

    Intellectual Property

Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.