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53 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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53 entries · page 1 of 2

  1. 15.02.02 · Risk Sub-Category

    Second-Order Risks

    Discrimination

    This is the risk of an ML system encoding stereotypes of or performing disproportionately poorly for some demographics/social groups.

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 1 1.1 AI Unintentional Post-deployment

  2. 15.02.04 · Risk Sub-Category

    Second-Order Risks

    Privacy

    The risk of loss or harm from leakage of personal information via the ML system.

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 2 2.1 AI Unintentional Post-deployment

  3. 15.02.03 · Risk Sub-Category

    Second-Order Risks

    Security

    This is the risk of loss or harm from intentional subversion or forced failure.

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 2 2.2 Human Intentional Post-deployment

  4. 15.02.07 · Risk Sub-Category

    Second-Order Risks

    Other ethical risks

    "Although we have discussed a number of common risks posed by ML systems, we acknowledge that there are many other ethical risks such as the potential for psychological manipulation, dehumanization, and exploitation of humans at scale."

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 4 4.1 Human Intentional Post-deployment

  5. 15.02.00 · Risk Category

    Second-Order Risks

    "Second-order risks result from the consequences of first-order risks and relate to the risks resulting from an ML system interacting with the real world, such as risks to human rights, the organization, and the natural environment."

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 6 6.0 Other Other Post-deployment

  6. 15.02.06 · Risk Sub-Category

    Second-Order Risks

    Organizational

    The risk of financial and/or reputational damage to the organization building or using the ML system.

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 6 6.0 Human Unintentional Post-deployment

  7. 15.02.05 · Risk Sub-Category

    Second-Order Risks

    Environmental

    The risk of harm to the natural environment posed by the ML system.

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 6 6.6 AI Unintentional Other

  8. 15.01.00 · Risk Category

    First-Order Risks

    "First-order risks can be generally broken down into risks arising from intended and unintended use, system design and implementation choices, and properties of the chosen dataset and learning components."

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.0 Other Other Other

  9. 15.01.01 · Risk Sub-Category

    First-Order Risks

    Application

    "This is the risk posed by the intended application or use case. It is intuitive that some use cases will be inherently "riskier" than others (e.g., an autonomous weapons system vs. a customer service chatbot)."

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.0 Human Intentional Post-deployment

  10. 15.01.04 · Risk Sub-Category

    First-Order Risks

    Training & validation data

    "This is the risk posed by the choice of data used for training and validation."

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.0 Human Other Pre-deployment

  11. 15.01.07 · Risk Sub-Category

    First-Order Risks

    Implementation

    "This is the risk of system failure due to code implementation choices or errors."

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.0 Human Unintentional Pre-deployment

  12. 15.01.08 · Risk Sub-Category

    First-Order Risks

    Control

    This is the difficulty of controlling the ML system

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.1 Other Other Other

  13. 15.01.09 · Risk Sub-Category

    First-Order Risks

    Emergent behavior

    "This is the risk resulting from novel behavior acquired through continual learning or self-organization after deployment."

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.1 AI Unintentional Post-deployment

  14. 15.01.02 · Risk Sub-Category

    First-Order Risks

    Misapplication

    This is the risk posed by an ideal system if used for a purpose/in a manner unintended by its creators. In many situations, negative consequences arise when the system is not used in the way or for the purpose it was intended.

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.3 Human Intentional Post-deployment

  15. 15.01.03 · Risk Sub-Category

    First-Order Risks

    Algorithm

    "This is the risk of the ML algorithm, model architecture, optimization technique, or other aspects of the training process being unsuitable for the intended application.Since these are key decisions that influence the final ML system, we capture their associated risks separately from design risks, even though they are part of the design process"

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.3 AI Unintentional Pre-deployment

  16. 15.01.05 · Risk Sub-Category

    First-Order Risks

    Robustness

    "This is the risk of the system failing or being unable to recover upon encountering invalid, noisy, or out-of-distribution (OOD) inputs."

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.3 AI Unintentional Post-deployment

  17. 15.01.06 · Risk Sub-Category

    First-Order Risks

    Design

    "This is the risk of system failure due to system design choices or errors."

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.3 Human Other Pre-deployment

  18. 15.02.01 · Risk Sub-Category

    Second-Order Risks

    Safety

    This is the risk of direct or indirect physical or psychological injury resulting from interaction with the ML system.

    From The Risks of Machine Learning Systems (Tan2022)

    Domain 7 7.3 Human Unintentional Post-deployment

  19. 15.01.01.a · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  20. 15.01.01.b · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  21. 15.01.01.c · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  22. 15.01.01.d · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  23. 15.01.01.e · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  24. 15.01.01.f · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  25. 15.01.01.g · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  26. 15.01.01.h · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  27. 15.01.01.i · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  28. 15.01.01.j · Additional evidence

    First-Order Risks

    Application

    —

    From The Risks of Machine Learning Systems (Tan2022)

  29. 15.01.02.a · Additional evidence

    First-Order Risks

    Misapplication

    —

    From The Risks of Machine Learning Systems (Tan2022)

  30. 15.01.02.b · Additional evidence

    First-Order Risks

    Misapplication

    —

    From The Risks of Machine Learning Systems (Tan2022)

  31. 15.01.02.c · Additional evidence

    First-Order Risks

    Misapplication

    —

    From The Risks of Machine Learning Systems (Tan2022)

  32. 15.01.03.a · Additional evidence

    First-Order Risks

    Algorithm

    —

    From The Risks of Machine Learning Systems (Tan2022)

  33. 15.01.03.b · Additional evidence

    First-Order Risks

    Algorithm

    —

    From The Risks of Machine Learning Systems (Tan2022)

  34. 15.01.03.c · Additional evidence

    First-Order Risks

    Algorithm

    —

    From The Risks of Machine Learning Systems (Tan2022)

  35. 15.01.04.a · Additional evidence

    First-Order Risks

    Training & validation data

    —

    From The Risks of Machine Learning Systems (Tan2022)

  36. 15.01.04.b · Additional evidence

    First-Order Risks

    Training & validation data

    —

    From The Risks of Machine Learning Systems (Tan2022)

  37. 15.01.04.c · Additional evidence

    First-Order Risks

    Training & validation data

    —

    From The Risks of Machine Learning Systems (Tan2022)

  38. 15.01.04.d · Additional evidence

    First-Order Risks

    Training & validation data

    —

    From The Risks of Machine Learning Systems (Tan2022)

  39. 15.01.04.e · Additional evidence

    First-Order Risks

    Training & validation data

    —

    From The Risks of Machine Learning Systems (Tan2022)

  40. 15.01.05.a · Additional evidence

    First-Order Risks

    Robustness

    —

    From The Risks of Machine Learning Systems (Tan2022)

  41. 15.01.05.b · Additional evidence

    First-Order Risks

    Robustness

    —

    From The Risks of Machine Learning Systems (Tan2022)

  42. 15.01.05.c · Additional evidence

    First-Order Risks

    Robustness

    —

    From The Risks of Machine Learning Systems (Tan2022)

  43. 15.01.06.a · Additional evidence

    First-Order Risks

    Design

    —

    From The Risks of Machine Learning Systems (Tan2022)

  44. 15.01.06.b · Additional evidence

    First-Order Risks

    Design

    —

    From The Risks of Machine Learning Systems (Tan2022)

  45. 15.01.06.c · Additional evidence

    First-Order Risks

    Design

    —

    From The Risks of Machine Learning Systems (Tan2022)

  46. 15.01.06.d · Additional evidence

    First-Order Risks

    Design

    —

    From The Risks of Machine Learning Systems (Tan2022)

  47. 15.01.06.e · Additional evidence

    First-Order Risks

    Design

    —

    From The Risks of Machine Learning Systems (Tan2022)

  48. 15.01.07.a · Additional evidence

    First-Order Risks

    Implementation

    —

    From The Risks of Machine Learning Systems (Tan2022)

  49. 15.01.07.b · Additional evidence

    First-Order Risks

    Implementation

    —

    From The Risks of Machine Learning Systems (Tan2022)

  50. 15.01.08.a · Additional evidence

    First-Order Risks

    Control

    —

    From The Risks of Machine Learning Systems (Tan2022)

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