Whole of AI lifecycle: statements 1 - 8

Whole of AI lifecycle includes statements that apply across multiple AI product lifecycle stages, for ease of use and to minimise content duplication.

The challenges for government use of AI are complex and linked with other governance considerations, such as:

  • the APS Code of Conduct
  • data governance
  • cyber security
  • ICT infrastructure
  • privacy
  • sourcing and procurement
  • copyright
  • ethics practices.

Across the lifecycle stages, agencies should consider:

  • technology operations – to ensure compliance, efficiency, and ethical standards
  • reference architecture – to provide structured frameworks that guide the design, development, and management of AI solutions
  • people capabilities – having the specialised skills required for successful implementation
  • auditability – enabling external scrutiny, supporting transparency, and accountability
  • explainability – identifying what needs to be explained and when, making complex AI processes transparent and trustworthy
  • system bias – maintaining the role of positive bias in delivering meaningful outcomes, while mitigating the source and impacts of problematic bias
  • version control – tracking and managing changes to information to inform stakeholder decision-making
  • watermarking – to embed visual or hidden markers into generated content so that its creation details can be identified.

Notes: 

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