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.

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39 entries

  1. 19.05.00 · Risk Category

    Ethical AI Risks

    "In the context of ethical AI risks, two risks are of particular importance. First, AI systems may lack a legitimate ethical basis in establishing rules that greatly influence society and human relationships (Wirtz & Müller, 2019). In addition, AI-based discrimination refers to an unfair treatment of certain population groups by AI systems. As humans initially programme AI systems, serve as their potential data source, and have an impact on the associated data processes and databases, human biases and prejudices may also become part of AI systems and be reproduced (Weyerer & Langer, 2019, 2020

    From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022)

  2. 19.01.03 · Risk Sub-Category

    Technological, Data and Analytical AI Risks

    Lack of data, poor data quality, and biases in training data

  3. 19.05.02 · Risk Sub-Category

    Ethical AI Risks

    Unfair statistical AI decisions and discrimination of minorities

  4. 19.04.02 · Risk Sub-Category

    Social AI Risks

    Privacy and safety concerns due to ubiquity of AI systems in economy and society (lack of social acceptance)

  5. 19.01.04 · Risk Sub-Category

    Technological, Data and Analytical AI Risks

    Vulnerability of AI systems to attacks and misuse

  6. "Informational and communicational AI risks refer particularly to informational manipulation through AI systems that influence the provision of information (Rahwan, 2018; Wirtz & Müller, 2019), AIbased disinformation and computational propaganda, as well as targeted censorship through AI systems that use respectively modified algorithms, and thus restrict freedom of speech."

    From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022)

  7. 19.02.01 · Risk Sub-Category

    Informational and Communicational AI Risks

    Manipulation and control of information provision (e.g., personalised adds, filtered news)

  8. 19.02.02 · Risk Sub-Category

    Informational and Communicational AI Risks

    Disinformation and computational propaganda

  9. 19.02.04 · Risk Sub-Category

    Informational and Communicational AI Risks

    Endangerment of data protection through AI cyberattacks

  10. 19.04.03 · Risk Sub-Category

    Social AI Risks

    Hazardous misuse of AI systems bears danger to the society in public spaces (e.g., hacker attacks on autonomous weapons)

  11. 19.04.05 · Risk Sub-Category

    Social AI Risks

    Decreasing human interaction as AI systems assume human tasks, disturbing well-being

  12. 19.02.03 · Risk Sub-Category

    Informational and Communicational AI Risks

    Censorship of opinions expressed in the Internet restricts freedom of expression

  13. 19.03.03 · Risk Sub-Category

    Economic AI Risks

    Loss of supervision and control of business processes

  14. 19.05.06 · Risk Sub-Category

    Ethical AI Risks

    AI systems may undermine human values (e.g., free will, autonomy)

  15. 19.06.02 · Risk Sub-Category

    Legal AI Risks

    Technology obedience and lack of governance through increasing application of AI systems

  16. 19.01.07 · Risk Sub-Category

    Technological, Data and Analytical AI Risks

    High investment costs of AI hinder integration

  17. 19.03.04 · Risk Sub-Category

    Economic AI Risks

    Financial feasibility and high investment costs for AI technology to remain competitive

  18. 19.03.05 · Risk Sub-Category

    Economic AI Risks

    Lack of AI strategy and acceptance/resistance among employees and customers

  19. 19.06.04 · Risk Sub-Category

    Legal AI Risks

    Hard legislation on AI hinders innovation processes and further AI development

  20. 19.03.00 · Risk Category

    Economic AI Risks

    "In the context of economic AI risks two major risks dominate. These refer to the disruption of the economic system due to an increase of AI technologies and automation. For instance, a higher level of AI integration into the manufacturing industry may result in massive unemployment, leading to a loss of taxpayers and thus negatively impacting the economic system (Boyd & Wilson, 2017; Scherer, 2016). This may also be associated with the risk of losing control and knowledge of organisational processes as AI systems take over an increasing number of tasks, replacing employees in these processes.

    From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022)

  21. 19.03.02 · Risk Sub-Category

    Economic AI Risks

    Replacement of humans and unemployment due to AI automation

  22. 19.04.00 · Risk Category

    Social AI Risks

    "Social AI risks particularly refer to loss of jobs (technological unemployment) due to increasing automation, reflected in a growing resistance by employees towards the integration of AI (Thierer et al., 2017; Winfield & Jirotka, 2018). In addition, the increasing integration of AI systems into all spheres of life poses a growing threat to privacy and to the security of individuals and society as a whole (Winfield & Jirotka, 2018; Wirtz et al., 2019)."

    From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022)

  23. 19.04.01 · Risk Sub-Category

    Social AI Risks

    Increasing social inequality

  24. 19.03.01 · Risk Sub-Category

    Economic AI Risks

    Disruption of economic systems (e.g., labour market, money value, tax system)

  25. 19.05.07 · Risk Sub-Category

    Ethical AI Risks

    Technological arms race with autonomous weapons

  26. 19.01.05 · Risk Sub-Category

    Technological, Data and Analytical AI Risks

    Lack of AI experts with comprehensive AI knowledge

  27. 19.04.04 · Risk Sub-Category

    Social AI Risks

    Lack of knowledge and social acceptance regarding AI

  28. 19.06.00 · Risk Category

    Legal AI Risks

    "Legal and regulatory risks comprise in particular the unclear definition of responsibilities and accountability in case of AI failures and autonomous decisions with negative impacts (Reed, 2018; Scherer, 2016). Another great risk in this context refers to overlooking the scope of AI governance and missing out on important governance aspects, resulting in negative consequences (Gasser & Almeida, 2017; Thierer et al., 2017)."

    From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022)

  29. 19.06.01 · Risk Sub-Category

    Legal AI Risks

    Unclear definition of responsibilities and accountability for AI judgments and their consequences

  30. 19.06.03 · Risk Sub-Category

    Legal AI Risks

    Great scope and ubiquity of AI make appropriate governance difficult, coverage of governance scope almost impossibl

  31. 19.06.05 · Risk Sub-Category

    Legal AI Risks

    Capturing future AI development and their threats with appropriate mechanism

  32. 19.01.02 · Risk Sub-Category

    Technological, Data and Analytical AI Risks

    Programming error

  33. 19.01.01 · Risk Sub-Category

    Technological, Data and Analytical AI Risks

    Loss of control of autonomous systems and unforeseen behaviour due to lack of transparency and self-programming/ reprogramming

  34. 19.01.06 · Risk Sub-Category

    Technological, Data and Analytical AI Risks

    Immaturity of AI technology can cause incorrect decisions

  35. 19.05.01 · Risk Sub-Category

    Ethical AI Risks

    AI sets rules without ethical basis

  36. 19.05.03 · Risk Sub-Category

    Ethical AI Risks

    Problem of defining human values for an AI system

  37. 19.05.04 · Risk Sub-Category

    Ethical AI Risks

    Misinterpretation of human value definitions/ ethics by AI systems

  38. 19.05.05 · Risk Sub-Category

    Ethical AI Risks

    Incompatibility of human vs. AI value judgment due to missing human qualities

  39. "Fig 3 shows that technological, data, and analytical AI risks are characterised by the loss of control over AI systems, whereby in particular the autonomous decision and its consequences are classified as risk factors since they are not subject to human influence (Boyd & Wilson, 2017; Scherer, 2016; Wirtz et al., 2019). Programming errors in algorithms due to the lack of expert knowledge or to the increasing complexity and black-box character of AI systems may also lead to undesired AI results (Boyd & Wilson, 2017; Danaher et al., 2017). In addition, a lack of data, poor data quality, and bia

    From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022)

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