Navigating the Landscape of AI Ethics and Responsibility
Provides a structured taxonomy of AI ethics risks that APS practitioners can cross-reference against existing Australian Government AI frameworks.
Key points
- A 2023 academic framework clusters AI ethics and responsibility issues into six groups via systematic literature review.
- The six clusters map closely to risk categories already recognised in Australian AI governance frameworks and agency guidance.
- This is a summary of an existing academic paper - useful context but not new primary guidance for APS practitioners.
Implications for Australian agencies
- Consider APS practitioners developing AI risk taxonomies or ethics frameworks could assess whether the six clusters align with or complement risk categories used in agency-level AI governance documentation.
- Monitor Teams tracking the MIT AI Risk Repository may want to monitor subsequent framework summaries in this blog series for higher-signal additions to the Repository.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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"Navigating the Landscape of AI Ethics and Responsibility"
Source: MIT AI Risk Repository – Blog
Published: 11 September 2024
URL: https://airisk.mit.edu/blog/navigating-the-landscape-of-ai-ethics-and-responsibility
MIT's AI Risk Repository blog summarises a 2023 academic framework by Cunha and Estima that uses systematic literature review and real-world news analysis to cluster AI ethics and responsibility issues into six groups: broken systems, hallucinations, intellectual property violations, privacy and regulation violations, enabling malicious actors, and environmental and socioeconomic harms. The framework recommends addressing AI ethics across five dimensions - research, education, development, operation, and business model. The blog post is a secondary summary of a Springer LNCS paper, not original primary research.
Implications for Australian agencies:
- [Consider] APS practitioners developing AI risk taxonomies or ethics frameworks could assess whether the six clusters align with or complement risk categories used in agency-level AI governance documentation.
- [Monitor] Teams tracking the MIT AI Risk Repository may want to monitor subsequent framework summaries in this blog series for higher-signal additions to the Repository.
Retrieved from SIMS, 18 July 2026.