Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction
A structured harm taxonomy for algorithmic systems offers APS governance teams a reference vocabulary for risk assessment and policy design.
Key points
- A 2023 peer-reviewed taxonomy classifies algorithmic harms into five categories and 20 subcategories across micro, meso, and macro levels.
- MIT AI Risk Repository spotlights this as one of its indexed risk frameworks, making it more accessible to practitioners.
- Published in 2023 and now featured in a repository blog - substantive but not a new or urgent development for APS readers.
Implications for Australian agencies
- Consider APS agencies developing AI risk registers or harm assessment frameworks could consider mapping the Shelby et al. taxonomy against existing departmental risk categories for gaps.
- Monitor Governance teams may want to monitor which frameworks the MIT AI Risk Repository continues to surface, as the repository is increasingly cited in international AI governance work.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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"Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction"
Source: MIT AI Risk Repository – Blog
Published: 16 January 2025
URL: https://airisk.mit.edu/blog/sociotechnical-harms-of-algorithmic-systems-scoping-a-taxonomy-for-harm-reduction
The MIT AI Risk Repository has spotlighted a 2023 AAAI/ACM paper by Shelby et al. that presents an applied taxonomy of sociotechnical harms from algorithmic systems, derived from a scoping review of 172 computing research papers. The taxonomy organises harms into five major categories - representational, allocative, quality of service, interpersonal, and social system harms - each with detailed subcategories. It frames impacts at micro, meso, and macro societal levels and is designed to support harm reduction in AI system design and governance. Its inclusion in the MIT AI Risk Repository increases its visibility as a reference framework for practitioners.
Implications for Australian agencies:
- [Consider] APS agencies developing AI risk registers or harm assessment frameworks could consider mapping the Shelby et al. taxonomy against existing departmental risk categories for gaps.
- [Monitor] Governance teams may want to monitor which frameworks the MIT AI Risk Repository continues to surface, as the repository is increasingly cited in international AI governance work.
Retrieved from SIMS, 18 July 2026.