The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration
A structured academic taxonomy of AI governance risks for public administration - useful context for APS teams building or reviewing their own risk frameworks.
Key points
- MIT AI Risk Repository spotlights a 2020 academic framework organising AI governance challenges for public administration into three categories.
- The framework's five-layer governance structure and four-stage regulatory process offer a reference model for agency AI risk management.
- The underlying paper is five years old; APS practitioners likely have more current frameworks already in use.
Implications for Australian agencies
- Monitor Agencies building or reviewing AI risk taxonomies may want to note this framework as a reference point, while prioritising more current sources such as the NIST AI RMF or APS AI Policy guidance.
- Consider Policy teams could consider whether the MIT AI Risk Repository's broader catalogue of risk frameworks is a useful secondary reference when scoping AI governance documentation.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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"The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration"
Source: MIT AI Risk Repository – Blog
Published: 16 July 2025
URL: https://airisk.mit.edu/blog/the-dark-sides-of-artificial-intelligence-an-integrated-ai-governance-framework-for-public-administration
The MIT AI Risk Repository has spotlighted a 2020 academic paper by Wirtz, Weyerer, and Sturm that presents an integrated AI governance framework for public administration. The framework organises AI challenges into three categories - law and regulation, societal impacts, and ethics - and proposes a five-layer governance structure alongside a four-stage regulatory process (framing, risk and benefit assessment, risk evaluation, and risk management). Grounded in regulation theory, it treats AI challenges as market failures requiring governmental intervention. The blog post summarises the framework as part of MIT's ongoing series cataloguing AI risk taxonomies.
Implications for Australian agencies:
- [Monitor] Agencies building or reviewing AI risk taxonomies may want to note this framework as a reference point, while prioritising more current sources such as the NIST AI RMF or APS AI Policy guidance.
- [Consider] Policy teams could consider whether the MIT AI Risk Repository's broader catalogue of risk frameworks is a useful secondary reference when scoping AI governance documentation.
Retrieved from SIMS, 18 July 2026.