Examples of inappropriate public generative AI use
When to apply
Apply Criterion 6 during the Discovery and Alpha phases to capture potential solutions, new and existing, that the service could use to solve problems.
Foster a culture of sharing experiences with other agencies, build on the learnings taken from them and align to common platforms, patterns and standards throughout the Service design and delivery process.
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Additional resources
- AI in government
Read government policies and initiatives on the safe, responsible use of AI in the APS. - Australia's AI Ethics Principles
Understand the ethical principles guiding responsible AI development and use in Australia. - GovAI
Access learning resources and tools for AI testing and exploration. - AI in Government Fundamentals course
Build foundational knowledge of AI in government through this self-paced online course offered by the APS Academy. - AI and Machine Learning Community of Practice
Online community for government personnel interested in AI, machine learning, deep learning and neural networks. Anyone with a gov.au email address can register for an APS Professions platform account. - Information Management for Records Created Using AI Technologies
National Archives of Australia guidance on identifying and managing Commonwealth records created by or relating to AI. - Engaging with AI
Cyber.gov.au advice for secure AI system development and deployment. - Privacy Guidance for AI Products
Learn how to manage privacy risks when using commercially available AI tools with the resource from the Office of the Australian Information Commissioner. - Protective Security Policy Framework
Access government guidance on safeguarding people, information and assets, including considerations for AI systems under the PSPF.
Assurance research series: 01
Solution
Technology expertise and acceptance
Often digital projects involve innovating with technologies that are unfamiliar or untested, which can affect delivery confidence.
Strategies to elevate confidence include iterative deployment strategies that build capability and confidence. Other areas for attention include interfaces with legacy systems, including the ways new systems interact with, or replace, aging legacy systems, while maintaining essential services.
Aging legacy systems can affect the system stability upon which the transformation may be reliant.
Legacy system dependencies need thorough analysis.
Assumptions about legacy data coherence and consistency can be particularly problematic, especially for projects involving transfer to cloud services.
More generally, the solution needs to conform to the technical architecture of the business area and the quality requirements of the business.
For commercial off the shelf software, the degree of fit with business requirements and degree of change that will be required to software or service can reduce delivery confidence.
For AI-based transformation, detailed understanding of how artificial intelligence (AI) will be used within the business environment is essential, as is consideration of human rights, privacy and ethics implications, particularly for AI.
Consideration should be given for AI solution reliability and safety, and the transparency, explainability and contestability of decisions made using AI solutions. For reference, the Australian Government has developed Australia’s AI Ethics Principles which are foundational to Australia’s safe and responsible adoption of AI.
The policy for the responsible use of AI in government builds on this foundation and aims to ensure that government plays a leadership role in embracing AI for the benefit of Australians.
Delivery Confidence Assessment (DCA) tolerances
High
In-house expertise in the technology. Ability to challenge supplier expertise. User excitement with the solution.
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Medium high
Significant in-house familiarity with the technology. User acceptance of the solution.
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Medium
Some in-house familiarity with the technology. User tolerance of the solution.
Off
Medium low
Low in-house experience with the technology. Largely reliant on supplier capability. Users have had minimal exposure to the technology.
Off
Low
No in-house familiarity with the technology. Complete reliance on contractor expertise.
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