Skip to content

AI Governance

From PECB

AI governance ensures ethical, safe, and responsible development and deployment of artificial intelligence technologies. It encompasses a set of rules, standards, and processes that guide AI research and applications, aiming to protect human rights and promote fairness, accountability, and transparency.

Governance helps to ensure AI systems are ethical, consistent with individual, company and societal principles, value-producing with successful results that benefit customers and businesses, and compliant: adherent to local, regional, national, and international laws.

Effective governance at appropriate institutional levels will improve results, whiel minimizing risks to customers and businesses. The challenge is in understanding what is right for your business.

Common Elements in AI Governance

Ethics: Principles to aim towards

Core elements in AI governance require ethics to guide AI governance. While there are many variations surrounding thes, from sources such as this one, they can include considerations such as the following:

  1. Human-centric: Amplifies the capabilities and protects the interests of people.
  2. Transparency: All aspects of the AI system and its development are thoughtfully described and documented.
  3. Fairness: Equitable and beneficial for all
  4. Explainability: The AI's results can be understood and reproduced
  5. Sustainability: Minimizes environmental impact
  6. Accountability: Enabling actions to be taken to prevent future failures
  7. Observability: Allows one to observe the AI to be evaluated
  8. Positive Impact: Creates positive value for all parties
  9. Private: Appropriately protects the privacy rights of people
  10. Secure: Cannot be mis-used intentionally or unintentionally

Responsible Development and Monitoring

Risk identification and Mitigation

Risk severity table from here

image

Lifecycle Maintenance

Observability

Feedback

What Governmence looks like

There are a number of resources all around the internet that may faciliate in understanding what should. be done. One example is the AI-Governance provides an example 'Hourglass Model' for organizations to organize their AI

The different components have associated tasks, which we take from here, helps to identify the different tasks that should be done throughout the lifecycl eof AI products.

Governance Lifecycle

image

These actions are described here

AI Governance To Do List
## A. AI System
T1. AI system repository and ID
T2. AI system pre-design
T3. AI system use case
T4. AI system user
T5. AI system operating environment
T6. AI system architecture
T7. AI system deployment metrics
T8. AI system operational metrics
T9. AI system version control design
T10. AI system performance monitoring design
T11. AI system health check design
T12. AI system verification and validation
T13. AI system approval
T14. AI system version control
T15. AI system performance monitoring
T16. AI system health checks
## B. Algorithms
T17. Algorithm ID
T18. Algorithm pre-design
T19. Algorithm use case design
T20. Algorithm technical environment design
T21. Algorithm deployment metrics design
T22. Algorithm operational metrics design
T23. Algorithm version control design
T24. Algorithm performance monitoring design
T25. Algorithm health checks design
T26. Algorithm verification and validation
T27. Algorithm approval
T28. Algorithm version control
T29. Algorithm performance monitoring
T30. Algorithm health checks
## C. Data operations
T33. Data pre-processing
T34. Data quality assurance
T31. Data sourcing
T32. Data ontologies, inferences, and proxies
T35. Data quality metrics
T36. Data quality monitoring design
T37. Data health check design
T38. Data quality monitoring
T39. Data health checks
## D. Risk and impacts
T40. AI system harms and impacts pre-assessment
T41. Algorithm risk assessment
T42. AI system health, safety and fundamental rights impact assessment
T43. AI system non-discrimination assurance
T44. AI system impact minimization
T45. AI system impact metrics design
T46. AI system impact monitoring design
T49. TEC expectation canvassing
T50. TEC design
T47. AI system impact monitoring
T48. AI system impact health check
## E. Transparency, explainability and contestability (TEC)
T51. TEC monitoring design
T52. TEC monitoring
T53. TEC health checks
## F. Accountability and ownership
T54. Head of AI
T55. AI system owner
T56. Algorithm owner
## G. Development and operations
T57. AI development
T58. AI operations
T59. AI governance integration
## H. Compliance
  T60. Regulatory canvassing
  T61. Regulatory risks, constraints, and design parameter analysis
  T62. Regulatory design review
  T63. Compliance monitoring design
  T64. Compliance health check design
  T65. Compliance assessment
  T66. Compliance monitoring
  T67. Compliance health checks

AI Governance Stakeholders

There are numerous and varied stakeholders that may be a part of any governance solution. Here is a general list that will necessarily vary depending on business structure:

  1. C-Suite level:
  2. CIO - Chief Information Officer
  3. CISO - Chief Information Security Officer
  4. CPO - Chief Privacy Officer
  5. CDO - Chief Data Officer
  6. Legal - Ensuring AI Compliance and security
  7. Communication - Presenting internal and external representations of stances towards AI
  8. System or application owner(s) - Those building overal products
  9. Software Architects and Developers
  10. AI/ML Engineers and Researchers - Creating AI solutions
  11. Data Scientists and Domain Experts - Helping to understand enable Data for use in AI systems
  12. UX - User Interfacing and Experience
  13. Users - Those who use the AI