Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study
Catalogues a widely-cited set of AI social impact categories that APS risk and governance practitioners can cross-reference against existing Australian frameworks.
Key points
- A 2023 systematic review of 175 articles identifies nine categories of AI social impact, led by bias and discrimination.
- MIT AI Risk Repository spotlights this as one of ten risk frameworks informing its broader AI risk taxonomy.
- The paper is a 2021 conference proceedings item; MIT's blog summary adds limited new content beyond the original framework.
Implications for Australian agencies
- Monitor Risk and governance teams may want to monitor the MIT AI Risk Repository as it expands its collection of risk frameworks for potential cross-referencing with APS AI governance work.
- Consider Agencies developing or reviewing AI risk taxonomies could consider whether the nine social impact categories identified align with or usefully supplement existing Australian Government risk categorisations.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study"
Source: MIT AI Risk Repository – Blog
Published: 3 January 2025
URL: https://airisk.mit.edu/blog/social-impacts-of-artificial-intelligence-and-mitigation-recommendations-an-exploratory-study
MIT's AI Risk Repository highlights a 2023 academic paper by Paes, Silveira, and Akkari that systematically reviewed 175 articles to identify nine main categories of AI social impact, including bias and discrimination, risk of injury, data breach and privacy, job displacement, and environmental impacts. Bias and discrimination was the most frequently mentioned category at 26%. The paper also catalogues mitigation strategies from the literature. It forms part of the MIT AI Risk Repository's curated collection of risk frameworks, which aggregates multiple taxonomies into a common reference resource.
Implications for Australian agencies:
- [Monitor] Risk and governance teams may want to monitor the MIT AI Risk Repository as it expands its collection of risk frameworks for potential cross-referencing with APS AI governance work.
- [Consider] Agencies developing or reviewing AI risk taxonomies could consider whether the nine social impact categories identified align with or usefully supplement existing Australian Government risk categorisations.
Retrieved from SIMS, 18 July 2026.