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

TL;DR

This paper develops a comprehensive taxonomy of sociotechnical harms from algorithmic systems, aiding practitioners in identifying and mitigating potential social and cultural impacts of technology.

Contribution

It presents the first systematic, taxonomy-based overview of sociotechnical harms from a broad review of computing research, integrating existing classifications into a unified framework.

Findings

Identifies five major themes of harms: representational, allocative, quality-of-service, interpersonal, and societal.

Provides a structured taxonomy to systematically surface potential harms in algorithmic systems.

Discusses challenges and opportunities for future research in harm mitigation.

Abstract

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.