Browse AI risks

2,500 entries · page 35 of 50

1. 16.06.02.b · Additional evidence

   Risk area 6: Environmental and Socioeconomic harms

   Increasing inequality and negative effects on job quality

   —

   From Taxonomy of Risks posed by Language Models (Weidinger2022)

2. 17.01.01.a · Additional evidence

   Discrimination, Exclusion and Toxicity

   Social stereotypes and unfair discrmination

   —

   From Ethical and social risks of harm from language models (Weidinger2021)

3. 17.01.02.a · Additional evidence

   Discrimination, Exclusion and Toxicity

   Exclusionary norms

   —

   From Ethical and social risks of harm from language models (Weidinger2021)

4. 17.01.02.b · Additional evidence

   Discrimination, Exclusion and Toxicity

   Exclusionary norms

   —

   From Ethical and social risks of harm from language models (Weidinger2021)

5. 17.01.02.c · Additional evidence

   Discrimination, Exclusion and Toxicity

   Exclusionary norms

   —

   From Ethical and social risks of harm from language models (Weidinger2021)

6. 17.01.04.a · Additional evidence

   Discrimination, Exclusion and Toxicity

   Lower performance for some languages and social groups

   —

   From Ethical and social risks of harm from language models (Weidinger2021)

7. 17.02.01.a · Additional evidence

   Information Hazards

   Compromising privacy by leaking private infiormation

   —

   From Ethical and social risks of harm from language models (Weidinger2021)

8. 17.02.01.b · Additional evidence

   Information Hazards

   Compromising privacy by leaking private infiormation

   —

   From Ethical and social risks of harm from language models (Weidinger2021)

9. 17.02.02.a · Additional evidence

   Information Hazards

   Compromising privacy by correctly inferring private information

   —

   From Ethical and social risks of harm from language models (Weidinger2021)

10. 17.02.03.a · Additional evidence

    Information Hazards

    Risks from leaking or correctly inferring sensitive information

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

11. 17.02.03.b · Additional evidence

    Information Hazards

    Risks from leaking or correctly inferring sensitive information

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

12. 17.03.01.a · Additional evidence

    Misinformation Harms

    Disseminating false or misleading information

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

13. 17.03.02.a · Additional evidence

    Misinformation Harms

    Causing material harm by disseminating false or poor information

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

14. 17.03.02.b · Additional evidence

    Misinformation Harms

    Causing material harm by disseminating false or poor information

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

15. 17.04.01.a · Additional evidence

    Malicious Uses

    Making disinformation cheaper and more effective

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

16. 17.04.01.b · Additional evidence

    Malicious Uses

    Making disinformation cheaper and more effective

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

17. 17.04.01.c · Additional evidence

    Malicious Uses

    Making disinformation cheaper and more effective

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

18. 17.04.02.a · Additional evidence

    Malicious Uses

    Facilitating fraud, scames and more targeted manipulation

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

19. 17.04.02.b · Additional evidence

    Malicious Uses

    Facilitating fraud, scames and more targeted manipulation

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

20. 17.05.02.a · Additional evidence

    Human-Computer Interaction Harms

    Creating avenues for exploiting user trust, nudging or manipulation

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

21. 17.05.02.b · Additional evidence

    Human-Computer Interaction Harms

    Creating avenues for exploiting user trust, nudging or manipulation

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

22. 17.05.02.c · Additional evidence

    Human-Computer Interaction Harms

    Creating avenues for exploiting user trust, nudging or manipulation

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

23. 17.05.03.a · Additional evidence

    Human-Computer Interaction Harms

    Promoting harmful stereotypes by implying gender or ethnic identity

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

24. 17.06.01.a · Additional evidence

    Automation, Access and Environmental Harms

    Environmental harms from operation LMs

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

25. 17.06.01.b · Additional evidence

    Automation, Access and Environmental Harms

    Environmental harms from operation LMs

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

26. 17.06.02.a · Additional evidence

    Automation, Access and Environmental Harms

    Increasing inequality and negative effects on job quality

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

27. 17.06.02.b · Additional evidence

    Automation, Access and Environmental Harms

    Increasing inequality and negative effects on job quality

    —

    From Ethical and social risks of harm from language models (Weidinger2021)

28. 19.01.00 · Risk Category

    Technological, Data and Analytical AI Risks

    "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)

    Not coded Not coded Not coded

29. 20.01.01.a · Additional evidence

    AI Law and Regulation

    Governance of autonomous intelligence systems

    —

    From The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration (Wirtz2020)

30. 20.03.02 · Risk Sub-Category

    AI Society

    Social acceptance and trust in AI

    "Social acceptance and trust in AI is highly interconnected with the other challenges mentioned. Acceptance and trust result from the extent to which an individual’s subjective expectation corresponds to the real effect of AI on the individual’s life. In the case of transparent and explainable AI, acceptance may be high but if an individual encounters harmful AI behavior like discrimination, acceptance for AI will eventually decline (COMEST, 2017).

    From The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration (Wirtz2020)

    Not coded Not coded Not coded

31. 21.01.00 · Risk Category

    Data-level risk

    N/A

    From Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)

    Not coded Not coded Not coded

32. 21.01.02.a · Additional evidence

    Data-level risk

    Dataset shift

    —

    From Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)

33. 21.01.02.b · Additional evidence

    Data-level risk

    Dataset shift

    —

    From Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)

34. 21.01.02.c · Additional evidence

    Data-level risk

    Dataset shift

    —

    From Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)

35. 21.01.04.a · Additional evidence

    —

    Adversarial attack

    —

    From Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)

36. 21.02.00 · Risk Category

    Model-level risk

    N/A

    From Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)

    Not coded Not coded Not coded

37. 21.02.01.b · Additional evidence

    Model-level risk

    Model misspecification

    —

    From Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)

38. 21.02.01.c · Additional evidence

    Model-level risk

    Model misspecification

    —

    From Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)

39. 21.02.01.d · Additional evidence

    Model-level risk

    Model misspecification

    —

    From Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)

40. 22.01.01.a · Additional evidence

    Malicious Use (Intentional)

    Bioterrorism

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

41. 22.01.01.b · Additional evidence

    Malicious Use (Intentional)

    Bioterrorism

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

42. 22.01.01.c · Additional evidence

    Malicious Use (Intentional)

    Bioterrorism

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

43. 22.01.01.d · Additional evidence

    Malicious Use (Intentional)

    Bioterrorism

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

44. 22.01.02.a · Additional evidence

    Malicious Use (Intentional)

    Unleashing AI Agents

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

45. 22.01.03.a · Additional evidence

    Malicious Use (Intentional)

    Persuasive AIs

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

46. 22.01.03.b · Additional evidence

    Malicious Use (Intentional)

    Persuasive AIs

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

47. 22.01.04.a · Additional evidence

    Malicious Use (Intentional)

    Concentration of Power

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

48. 22.01.04.b · Additional evidence

    Malicious Use (Intentional)

    Concentration of Power

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

49. 22.01.04.c · Additional evidence

    Malicious Use (Intentional)

    Concentration of Power

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

50. 22.02.01.a · Additional evidence

    AI Race (Environmental/Structural)

    Military AI Arms Race

    —

    From An Overview of Catastrophic AI Risks (Hendrycks2023)

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