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

Renee Shelby, Shalaleh Rismani, Kate Henne, Ajung Moon, Negar Rostamzaheh, Paul Nicholas, N'Mah Yilla-Akbari, Jess Gallegos, Andrew Smart, Emilio Garcia, Gurleen Virk

    Research output: Contribution to conferencePaper


    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.
    Original languageEnglish
    Publication statusPublished - 2023
    EventAIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society - Montr'eal QC Canada
    Duration: 1 Jan 2023 → …


    ConferenceAIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society
    Period1/01/23 → …

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