AI Risk Profiles: A Standards Proposal for Pre-deployment AI Risk Disclosures
A structured pre-deployment risk disclosure taxonomy could inform how Australian agencies assess AI products at the procurement or deployment stage.
Key points
- Sherman and Eisenberg propose a nine-category AI risk taxonomy as a pre-deployment disclosure standard.
- The framework is applied to Claude, GPT APIs, Microsoft Copilot, GitHub Copilot, and Midjourney as worked examples.
- The taxonomy aims to bridge technical and non-technical stakeholders - useful for procurement and regulatory contexts.
Implications for Australian agencies
- Consider Agencies developing AI risk assessment or procurement processes could assess whether this nine-category taxonomy aligns with or complements existing frameworks such as the DTA's responsible AI policy guidance.
- Monitor Risk and governance teams may want to monitor the MIT AI Risk Repository as it catalogues multiple risk frameworks - useful for benchmarking agency-level approaches.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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"AI Risk Profiles: A Standards Proposal for Pre-deployment AI Risk Disclosures"
Source: MIT AI Risk Repository – Blog
Published: 16 January 2025
URL: https://airisk.mit.edu/blog/ai-risk-profiles-a-standards-proposal-for-pre-deployment-ai-risk-disclosures
A 2024 AAAI conference paper, spotlighted by the MIT AI Risk Repository, proposes a standardised AI risk profile framework built around nine high-level categories: Abuse & Misuse, Compliance, Environmental & Societal Impact, Explainability & Transparency, Fairness & Bias, Long-term & Existential Risk, Performance & Robustness, Privacy, and Security. The framework is designed to guide procurement decisions, triage further risk assessment, and inform regulatory frameworks. It is applied to several commercial AI systems as practical examples, and is intended as a shared vocabulary bridging technical and non-technical stakeholders. The MIT AI Risk Repository includes this as one of eleven risk frameworks in its collection.
Implications for Australian agencies:
- [Consider] Agencies developing AI risk assessment or procurement processes could assess whether this nine-category taxonomy aligns with or complements existing frameworks such as the DTA's responsible AI policy guidance.
- [Monitor] Risk and governance teams may want to monitor the MIT AI Risk Repository as it catalogues multiple risk frameworks - useful for benchmarking agency-level approaches.
Retrieved from SIMS, 18 July 2026.