Browse AI risks

2,500 entries · page 37 of 50

1. 24.06.00.d · Additional evidence

   Appropriate Relationships

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   From The Ethics of Advanced AI Assistants (Gabriel2024)

2. 25.02.00.a · Additional evidence

   Deception

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   From Model Evaluation for Extreme Risks (Shevlane2023)

3. 26.01.00 · Risk Category

   Transparency

   "Ability to provide responsible disclosure to those affected by AI systems to understand the outcome"

   From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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4. 26.02.00 · Risk Category

   Explainability

   "Ability to assess the factors that led to the AI system's decision, its overall behaviour, outcomes, and implications"

   From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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5. 26.03.00 · Risk Category

   Repeatability / Reproducibility

   "The ability of a system to consistently perform its required functions under stated conditions for a specific period of time, and for an independent party to produce the same results given similar inputs"

   From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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6. 26.04.00 · Risk Category

   Safety

   "AI should not result in harm to humans (particularly physical harm), and measures should be put in place to mitigate harm"

   From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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7. 26.05.00 · Risk Category

   Security

   "AI security is the protection of AI systems, their data, and the associated infrastructure from unauthorised access, disclosure, modification, destruction, or disruption. AI systems that can maintain confidentiality, integrity, and availability through protection mechanisms that prevent unauthorized access and use may be said to be secure."

   From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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8. 26.06.00 · Risk Category

   Robustness

   "AI system should be resilient against attacks and attempts at manipulation by third party malicious actors, and can still function despite unexpected input"

   From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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9. 26.07.00 · Risk Category

   Fairness

   "AI should not result in unintended and inappropriate discrimination against individuals or groups"

   From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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10. 26.08.00 · Risk Category

    Data Governance

    "Governing data used in AI systems, including putting in place good governance practices for data quality, lineage, and compliance"

    From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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11. 26.09.00 · Risk Category

    Accountability

    "AI systems should have organisational structures and actors accountable for the proper functioning of AI systems"

    From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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12. 26.10.00 · Risk Category

    Human Agency & Oversight

    "Ability to implement appropriate oversight and control measures with humans-in-the-loop at the appropriate juncture"

    From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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13. 26.11.00 · Risk Category

    Inclusive Growth, Societal & Environmental Well-being

    "This Principle highlights the potential for trustworthy AI to contribute to overall growth and prosperity for all – individuals, society, and the planet – and advance global development objectives"

    From Summary Report: Binary Classification Model for Credit Risk (AIVerify2023)

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14. 27.01.00 · Risk Category

    Typical safety scenarios

    "First, We extend the dialogue safety taxonomy (Sun et al., 2022) and try to cover all perspectives of safety issues. It involves 8 kinds of typical safety scenarios such as insult and unfairness."

    From Safety Assessment of Chinese Large Language Models (Sun2023)

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15. 27.01.03.a · Additional evidence

    Typical safety scenarios

    Crimes and Illegal Activities

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    From Safety Assessment of Chinese Large Language Models (Sun2023)

16. 27.01.05.a · Additional evidence

    Typical safety scenarios

    Physical Harm

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    From Safety Assessment of Chinese Large Language Models (Sun2023)

17. 27.01.06.a · Additional evidence

    Typical safety scenarios

    Mental Health

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    From Safety Assessment of Chinese Large Language Models (Sun2023)

18. 27.01.07.a · Additional evidence

    Typical safety scenarios

    Privacy and Property

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    From Safety Assessment of Chinese Large Language Models (Sun2023)

19. 27.01.08.a · Additional evidence

    Typical safety scenarios

    Ethics and Morality

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    From Safety Assessment of Chinese Large Language Models (Sun2023)

20. 27.02.01.a · Additional evidence

    Instruction Attacks

    Goal Hijacking

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    From Safety Assessment of Chinese Large Language Models (Sun2023)

21. 27.02.04.a · Additional evidence

    Instruction Attacks

    Unsafe Instruction Topic

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    From Safety Assessment of Chinese Large Language Models (Sun2023)

22. 29.01.00 · Risk Category

    AI Trust Management

    individuals are more persuaded to use and depend on AI systems when they perceive them as reliable

    From Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions (Habbal2024)

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23. 29.02.00 · Risk Category

    AI Risk Management

    AI risk involves identifying possible threats and risks associated with AI systems. It encompasses examining the competences, constraints, and possible failure modes of AI technologies.

    From Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions (Habbal2024)

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24. 29.02.03.a · Additional evidence

    AI Risk Management

    Lethal Autonomous Weapons Systems (LAWS)

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    From Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions (Habbal2024)

25. 29.03.00 · Risk Category

    AI Security Management

    AI security management involves the adoption of practices and measures aimed at protecting AI systems and the data they process from unauthorized ac-cess, breaches, and malicious activities

    From Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions (Habbal2024)

    Not coded Not coded Not coded

26. 30.01.00.a · Additional evidence

    Reliability

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

27. 30.01.02.a · Additional evidence

    Reliability

    Hallucination

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

28. 30.01.05.a · Additional evidence

    Reliability

    Sychopancy

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

29. 30.01.05.b · Additional evidence

    Reliability

    Sychopancy

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

30. 30.02.03.a · Additional evidence

    Safety

    Harms to Minor

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

31. 30.02.05 · Risk Sub-Category

    Safety

    Mental Health Issues

    unhealthy interactions with Internet discussions can reinforce users’ mental issues

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

    Not coded Not coded Not coded

32. 30.02.06.a · Additional evidence

    Safety

    Privacy Violation

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

33. 30.03.00.a · Additional evidence

    Fairness

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

34. 30.03.00.b · Additional evidence

    Fairness

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

35. 30.03.01.a · Additional evidence

    Fairness

    Injustice

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

36. 30.03.03.a · Additional evidence

    Fairness

    Preference Bias

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

37. 30.03.03.b · Additional evidence

    Fairness

    Preference Bias

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

38. 30.04.03.a · Additional evidence

    Resistance to Misuse

    Social-Engineering

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

39. 30.04.03.b · Additional evidence

    Resistance to Misuse

    Social-Engineering

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

40. 30.06.01.a · Additional evidence

    Social Norm

    Toxicity

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    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

41. 31.05.00.a · Additional evidence

    Impact on Intellectual Property Rights

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    From Generating Harms - Generative AI's impact and paths forwards (EPIC2023)

42. 31.05.00.b · Additional evidence

    Impact on Intellectual Property Rights

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    From Generating Harms - Generative AI's impact and paths forwards (EPIC2023)

43. 31.07.01 · Risk Sub-Category

    Labor Manipulation, Theft, and Displacement

    Generative AI in the Workplace

    "The development of AI as a whole is changing how companies design their workplace and business models. Generative AI is no different. Time will tell whether and to what extent employers will adopt, implement, and integrate generative AI in their workplaces—and how much it will impact workers."

    From Generating Harms - Generative AI's impact and paths forwards (EPIC2023)

    Not coded Not coded Not coded

44. 32.02.00 · Risk Category

    Individual needs

    "The second group pertains to individual needs, such as safety and autonomy which are also reflected in informed consent and the avoidance of harm. Issues include Dignity, Safety, Harm to human capabilities, Autonomy, Ability to think one's own thoughts and form one's own opinions, Informed consent

    From The Ethics of ChatGPT – Exploring the Ethical Issues of an Emerging Technology (Stahl2024)

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45. 32.03.00 · Risk Category

    Culture and identity

    Supportive of culture and cultural diversity, Collective human identity and the good life

    From The Ethics of ChatGPT – Exploring the Ethical Issues of an Emerging Technology (Stahl2024)

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46. 33.01.00 · Risk Category

    Ethical Concerns

    "Ethics refers to systematizing, defending, and recommending concepts of right and wrong behavior (Fieser, n.d.). In the context of AI, ethical concerns refer to the moral obligations and duties of an AI application and its creators (Siau & Wang, 2020). Table 1 presents the key ethical challenges and issues associated with generative AI. These challenges include harmful or inappropriate content, bias, over-reliance, misuse, privacy and security, and the widening of the digital divide."

    From Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration (Nah2023)

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47. 33.04.00 · Risk Category

    Challenges associated with the economy:

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    From Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration (Nah2023)

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48. 36.01.00 · Risk Category

    Trust Concerns

    "These concerns encompass issues such as data privacy, technology misuse, errors in machine actions, bias, technology robustness, inexplicability, and transparency."

    From Benefits or Concerns of AI: A Multistakeholder Responsibility (Sharma2024)

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49. 36.02.00 · Risk Category

    Ethical Concerns

    "The second category encompasses ethical concerns associated with AI, including unemployment and job displacement, inequality, unfairness, social anxiety, loss of human skills and redundancy, and the human-machine symbiotic relationship."

    From Benefits or Concerns of AI: A Multistakeholder Responsibility (Sharma2024)

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50. 36.03.00 · Risk Category

    Disruption Concerns

    "Lastly, the third category of concerns pertains to the disruption of social and organizational culture, supply chains, and power structures caused by AI."

    From Benefits or Concerns of AI: A Multistakeholder Responsibility (Sharma2024)

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