Mapping the ethics of generative AI: A comprehensive scoping review
A structured taxonomy of generative AI ethics issues gives APS practitioners a literature-grounded checklist for risk identification and governance design.
Key points
- A scoping review identifies 378 normative issues across 19 topic areas in generative AI ethics literature.
- The taxonomy covers areas directly relevant to APS AI governance: fairness, hallucinations, transparency, evaluation, and alignment.
- The MIT AI Risk Repository context makes this a useful reference for agencies building AI risk registers or ethics frameworks.
Implications for Australian agencies
- Consider Agencies developing or refining AI risk registers could consider cross-referencing Hagendorff's taxonomy to identify gaps in their current risk coverage.
- Monitor Policy teams may want to monitor the MIT AI Risk Repository more broadly as a curated source of structured AI risk and ethics frameworks.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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"Mapping the ethics of generative AI: A comprehensive scoping review"
Source: MIT AI Risk Repository – Blog
Published: 25 September 2024
URL: https://airisk.mit.edu/blog/mapping-the-ethics-of-generative-ai-a-comprehensive-scoping-review
This MIT AI Risk Repository blog post summarises Thilo Hagendorff's 2024 scoping review, which maps the ethics of generative AI across 378 normative issues in 19 topic areas including fairness, safety, hallucinations, privacy, cybercrime, and governance. The taxonomy is ranked by prevalence in academic literature and is available as an interactive online tool. The review also notes imbalances in the literature, such as disproportionate focus on certain risks and under-evidenced risk scenarios. For APS practitioners, the taxonomy offers a structured reference point when developing AI risk registers, ethics frameworks, or procurement evaluation criteria.
Implications for Australian agencies:
- [Consider] Agencies developing or refining AI risk registers could consider cross-referencing Hagendorff's taxonomy to identify gaps in their current risk coverage.
- [Monitor] Policy teams may want to monitor the MIT AI Risk Repository more broadly as a curated source of structured AI risk and ethics frameworks.
Retrieved from SIMS, 18 July 2026.