  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.
