MIT AI Risk Repository

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9 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.

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9 entries

  1. 12.05.00 · Risk Category

    Fairness & Bias

    "The potential for AI systems to make decisions that systematically disadvantage certain groups or individuals. Bias can stem from training data, algorithmic design, or deployment practices, leading to unfair outcomes and possible legal ramifications."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

  2. 12.08.00 · Risk Category

    Privacy

    "The potential for the AI system to infringe upon individuals' rights to privacy, through the data it collects, how it processes that data, or the conclusions it draws."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

  3. 12.09.00 · Risk Category

    Security

    "Encompasses vulnerabilities in AI systems that compromise their integrity, availability, or confidentiality. Security breaches could result in significant harm, ranging from flawed decision-making to data leaks. Of special concern is leakage of AI model weights, which could exacerbate other risk areas."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

  4. 12.01.00 · Risk Category

    Abuse & Misuse

    "The potential for AI systems to be used maliciously or irresponsibly, including for creating deepfakes, automated cyber attacks, or invasive surveillance systems. Specifically denotes intentional use of AI for harm."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

  5. "Addresses AI's broader societal effects, including labor displacement, mental health impacts, and issues from manipulative technologies like deepfakes. Additionally, it considers AI's environmental footprint, balancing resource strain and training-related carbon emissions against AI's potential to help address environmental problems."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

  6. 12.02.00 · Risk Category

    Compliance

    "The potential for AI systems to violate laws, regulations, and ethical guidelines (including copyrights). Non-compliance can lead to legal penalties, reputation damage, and loss of trust.While other risks in our taxonomy apply to system developers, users, and broader society, this risk is generally restricted to the former two groups."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

  7. "The speculative potential for future advanced AI systems to harm human civilization, either through misuse or due to challenges in aligning AI objectives with human values."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

  8. "The AI system's ability to fulfill its intended purpose and its resilience to perturbations, and unusual or adverse inputs. Failures of performance are fundamental to the AI system's correct functioning. Failures of robustness can lead to severe consequences."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

  9. "The feasibility of understanding and interpreting an AI system's decisions and actions, and the openness of the developer about the data used, algorithms employed, and decisions made. Lack of these elements can create risks of misuse, misinterpretation, and lack of accountability."

    From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023)

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