mlsanityData to 14 Oct 2026

Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction

arXiv 2210.05791 ↗First posted 11 Oct 2022cs.HC cs.GLcited by 138

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Abstract (arXiv)

Understanding the landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences of the systems they build. It also supports the prospect of incorporating controls to help minimize harms that emerge from the interplay of technologies and social and cultural dynamics. A growing body of scholarship has identified a wide range of harms across different algorithmic technologies. However, computing research and practitioners lack a high level and synthesized overview of harms from algorithmic systems. Based on a scoping review of computing research (n=172), we present an applied taxonomy of sociotechnical harms to support a more systematic surfacing of potential harms in algorithmic systems. The final taxonomy builds on and refers to existing taxonomies, classifications, and terminologies. Five major themes related to sociotechnical harms - representational, allocative, quality-of-service, interpersonal harms, and social system/societal harms - and sub-themes are presented along with a description of these categories. We conclude with a discussion of challenges and opportunities for future research.

Key facts. Cited by 138 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Oct 2022.

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Citing papers per year: 2022: 1; 2023: 13; 2024: 46; 2025: 49; 2026: 29.

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  27. 2024Who's in and who's out? A case study of multimodal CLIP-filtering in DataCompcited by 15
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  30. 2025How Deep Is Representational Bias in LLMs? The Cases of Caste and Religioncited by 14
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