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

By R. Shelby, Shalaleh Rismani, Kathryn Henne, AJung Moon, Negar Rostamzadeh, P. Nicholas, N'Mah Yilla-Akbari, Jess Gallegos, Andrew Smart, Emilio Garcia and 1 more. 2022

390 citations recorded by the source corpus.

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.

Subfields: Computer Science Applications, Safety Research, Sociology and Political Science, AI Ethics, Fairness & Society

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Citations in the corpus

GPT-4o System Card (2024, background): “This can lead to both allocative and representational harms [13, 15] depending on how such behavior manifests.”