MIT AI Risk Repository

Browse AI risks

2 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.

2 entries

  1. 45.01.05 · Risk Sub-Category

    AI's inherent safety risks

    Risks from models and algorithms (Risks of unreliable output)

    "Generative AI can cause hallucinations, meaning that an AI model generates untruthful or unreasonable content but presents it as if it were a fact, leading to biased and misleading information."

    From AI Safety Governance Framework (TC2602024)

  2. 45.02.02 · Risk Sub-Category

    Safety risks in AI Applications

    Cyberspace risks (Risks of confusing facts, misleading users, and bypassing authentication)

    "AI systems and their outputs, if not clearly labeled, can make it difficult for users to discern whether they are interacting with AI and to identify the source of generated content. This can impede users' ability to determine the authenticity of information, leading to misjudgment and misunderstanding. Additionally, AI-generated highly realistic images, audio, and videos may circumvent existing identity verification mechanisms, such as facial recognition and voice recognition, rendering these authentication processes ineffective."

    From AI Safety Governance Framework (TC2602024)

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