⚡ Quick Summary
Published by the Australian Government and authored by the Australian, state and territory governments, this framework sets out a nationally consistent approach to the assurance of artificial intelligence use in government. Version 1.0 was published on 21 June 2024, and the approach is based on Australia's AI Ethics Principles (DISR 2019).
The framework is deliberately not technical. Instead of prescribing technical detail, it establishes cornerstones and practices of AI assurance, with jurisdictions expected to develop specific policies and guidance considerate of their own legislative, policy and operational context. Assurance is framed as part of the broader governance of how governments develop, procure and deploy AI, enabling them to understand expected benefits, identify risks and apply mitigations, ensure lawful use, understand whether AI is operating as expected, and demonstrate through evidence that use is safe and responsible.
The main deliverable is a set of implementation practices mapped to the eight AI Ethics Principles: human, societal and environmental wellbeing; human-centred values; fairness; privacy protection and security; reliability and safety; transparency and explainability; contestability; and accountability. Each principle carries numbered practices, from documenting intentions, consulting stakeholders and assessing impact to piloting, testing, monitoring and being prepared to disengage a system.
🧩 What’s Covered
The document moves from a ministerial statement, through the cornerstones of assurance, to practices mapped against each of the eight AI ethics principles.
- Statement from Data and Digital Ministers: Ministers commit the governments to being "exemplars in the safe and responsible use of AI" and present the framework as a step towards public confidence and trust, based on a lawful, ethical approach that places people first.
- Introduction: States the purpose as a nationally consistent approach to AI assurance and lists what assurance enables, including understanding expected benefits, identifying risks and applying mitigations, ensuring lawful use, understanding if AI operates as expected, and demonstrating safety and responsibility through evidence.
- Complementary initiatives: Positions the framework alongside Australia's AI Ethics Framework (DISR 2019), the Australian Government's consultation on safe and responsible AI, the NSW Artificial Intelligence Assurance Framework (Digital NSW 2022), the OECD principles, the Bletchley Declaration and the three outcomes agreed at the AI Seoul Summit on 21 May 2024.
- What is an AI system?: Reproduces the OECD definition approved in November 2023 and gives practical triggers for when assurance processes apply, including when a vendor describes a product as using AI.
- Cornerstones of assurance: Covers governance and data governance, a case-by-case risk-based approach using the OECD's four AI lifecycle phases, alignment to standards such as AS ISO/IEC 42001:2023 and AS ISO/IEC 23894:2023, and procurement considerations including contractual adaptability, vendor lock-in, due diligence and 'black box' systems.
- In focus boxes: Address risk-based regulation and the mandatory guardrails under consideration for high-risk settings, and the responsible use of generative AI, listing Australian government guidance on public generative AI tools and the common emphasis on human oversight and accountability.
- Implementing Australia's AI Ethics Principles: Maps practices to the eight principles, with numbered actions covering impact assessment, stakeholder consultation, human rights and anti-discrimination compliance, digital inclusion, privacy impact assessments, security guidance, pilot studies, red teaming, monitoring, AI registers, explainability, contestability channels and clear accountability roles.
- Resources: A reference list of Australian and international sources, with URLs and access dates.
💡 Why it matters?
For public sector bodies, the framework supplies a shared vocabulary and a reference set of practices for assuring AI before, during and after deployment, which matters where agencies must show that automated or AI-influenced decisions are lawful, fair and explainable. It links assurance to governance, data management, procurement and standards rather than treating it as a technical check, and it points to specific instruments such as privacy impact assessments, human rights impact assessments and the NSW assurance framework. It also signals how national assurance work relates to the mandatory guardrails being considered for high-risk settings and to international commitments Australia has agreed to.
❓ What’s Missing
The framework explicitly avoids technical detail, so it offers no thresholds, metrics, evaluation methods or templates for deciding how much assurance a given use case needs, leaving risk tolerance and proportionality to individual jurisdictions. Binding obligations are not defined, and the division of responsibilities between Australian, state and territory governments is left to separate policies. Several referenced instruments are dated or still developing, including the mandatory guardrails under consideration and an ISO impact assessment standard cited as a draft. The procurement and generative AI sections give direction rather than model clauses or checklists, and no evidence on the framework's application in practice is presented.
👥 Best For
Public sector governance, assurance and audit teams translating the AI ethics principles into agency-level processes, and policy officers coordinating AI guidance across jurisdictions. It also suits procurement and legal staff assessing contracts for AI systems and records, privacy or security specialists looking for where their obligations fit within an assurance lifecycle.
📄 Source Details
National framework for the assurance of artificial intelligence in government: A joint approach to safe and responsible AI by the Australian, state and territory governments, authored by the Australian, state and territory governments and published by the Australian Government, version 1.0, 21 June 2024. The PDF runs to 34 pages, with internal pagination to 31 plus front matter, under a Creative Commons BY 4.0 licence. The full text of all 34 pages was available for this review. No URL for the document itself is printed; the reference list cites external sources only.
