Artificial Intelligence Impact Assessment Tool

⚡ Quick Summary

The Artificial Intelligence Impact Assessment Tool is a comprehensive, government-grade framework designed to operationalise responsible AI principles across the full lifecycle of public-sector AI use cases. Developed by the Australian Digital Transformation Agency, the tool translates high-level AI Ethics Principles into a structured, auditable assessment process. It combines an initial threshold risk assessment with a deeper, principle-by-principle analysis covering fairness, safety, privacy, transparency, contestability, human-centred values and accountability. The result is not just a compliance checklist, but a decision-support instrument that forces teams to articulate purpose, justify the use of AI over non-AI alternatives, identify affected stakeholders, and document governance choices in a way that can withstand internal and external scrutiny. Its design reflects a mature governance mindset: AI is treated as a socio-technical system embedded in legal, institutional and societal contexts, not merely a technical deployment.

🧩 What’s Covered

The tool is structured into twelve sequential sections that mirror the lifecycle of an AI use case. It starts with basic contextualisation: naming the use case, identifying accountable owners, approving officers, contributors, and clearly describing how AI is used in plain language. It then requires teams to define the problem being solved, articulate the purpose of AI use, consider non-AI alternatives, and map stakeholders who may be positively or negatively affected.

A central element is the inherent risk assessment, which evaluates AI-specific risks before mitigation, using defined likelihood and consequence scales. This threshold assessment determines whether a use case can proceed with light-touch governance or must undergo a full assessment. For higher-risk cases, the tool guides teams through detailed evaluations aligned with Australia’s AI Ethics Principles: fairness, reliability and safety, privacy and security, transparency and explainability, contestability of outcomes, respect for human rights and diversity, and clear accountability mechanisms.

The final sections focus on alignment with applicable legal frameworks, documentation of decisions, and next steps, including monitoring, evaluation, and mandatory re-validation when systems change post-deployment. A separate guidance document mirrors each section, providing interpretative support, examples, and risk-consequence tables to ensure consistent application across agencies.

💡 Why it matters?

This tool shows how AI governance can move from abstract principles to concrete administrative practice. It demonstrates that impact assessments can function as living governance artefacts rather than one-off compliance exercises. For jurisdictions implementing or preparing for regimes like the EU AI Act, it offers a pragmatic blueprint for integrating ethics, risk management, administrative law and accountability into a single workflow. Importantly, it reinforces the idea that responsible AI is a continuous obligation, extending beyond procurement and deployment into monitoring, review and re-validation over time.

❓ What’s Missing

While robust for public-sector contexts, the tool is tightly coupled to Australian administrative structures and legal assumptions, which may limit direct transferability to private-sector or cross-border deployments. It also focuses primarily on individual use cases, offering less guidance on portfolio-level AI governance, model reuse across agencies, or systemic risks emerging from interacting systems. More explicit treatment of foundation models, generative AI supply chains, and third-party model risk would strengthen its future relevance.

👥 Best For

Public-sector organisations, regulators, and policy teams designing AI governance processes; practitioners seeking a concrete example of how to operationalise AI ethics principles; and private-sector governance leaders looking for inspiration on how to structure internal AI impact assessments with real decision-making weight.