Governance of artificial intelligence: A risk and guideline-based integrative framework
A public-sector-focused AI risk taxonomy with structured governance process steps could usefully inform APS risk assessment and governance framework design.
Key points
- A 2022 academic framework proposes six AI risk categories specifically designed for public sector governance contexts.
- The taxonomy links technological, ethical, legal, social, economic, and informational risks to concrete governance guidelines.
- MIT AI Risk Repository blog spotlight - the underlying paper is three years old and the signal is retrospective rather than new.
Implications for Australian agencies
- Consider APS teams developing or reviewing AI risk frameworks could consider referencing this taxonomy as a structured baseline for categorising public-sector AI risks.
- Monitor Practitioners may want to monitor the MIT AI Risk Repository more broadly as it surfaces additional frameworks relevant to government AI governance.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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"Governance of artificial intelligence: A risk and guideline-based integrative framework"
Source: MIT AI Risk Repository – Blog
Published: 16 July 2025
URL: https://airisk.mit.edu/blog/governance-of-artificial-intelligence-a-risk-and-guideline-based-integrative-framework
The MIT AI Risk Repository has spotlighted a 2022 academic paper by Wirtz, Weyerer, and Kehl that presents a six-category AI risk taxonomy developed specifically for public sector and government contexts. Categories span technological/data risks, informational/communicational risks, economic, social, ethical, and legal/regulatory risks. The framework includes a four-stage risk-oriented governance process and a seven-stage implementation layer intended to translate guidelines into binding governance measures. While not new, the taxonomy's public-sector emphasis and integrative structure may offer useful reference material for APS practitioners designing or reviewing AI governance frameworks.
Implications for Australian agencies:
- [Consider] APS teams developing or reviewing AI risk frameworks could consider referencing this taxonomy as a structured baseline for categorising public-sector AI risks.
- [Monitor] Practitioners may want to monitor the MIT AI Risk Repository more broadly as it surfaces additional frameworks relevant to government AI governance.
Retrieved from SIMS, 18 July 2026.