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

2023 inproceedings shelby ○ Not yet verified

Authors

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

Venue

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society

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.

BibTeX

Local Entry

@inproceedings{shelby,
  title = {Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction},
  author = {Shelby, Renee and Rismani, Shalaleh and Henne, Kathryn and Moon, AJung and Rostamzadeh, Negar and Nicholas, Paul and Yilla-Akbari, N'Mah and Gallegos, Jess and Smart, Andrew and Garcia, Emilio and Virk, Gurleen},
  year = {2023},
  booktitle = {Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society},
  doi = {10.1145/3600211.3604673},
  url = {https://doi.org/10.1145/3600211.3604673},
  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.},
  pages = {723–741},
  publisher = {Association for Computing Machinery}
}

From OPENALEX

@inproceedings{shelby,
  title = {Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction},
  author = {Renee Shelby and Shalaleh Rismani and Kathryn Henne and AJung Moon and Negar Rostamzadeh and P. O. Nicholas and N'Mah Yilla-Akbari and Jess Gallegos and Andrew Smart and Emilio García García and Gurleen Virk},
  year = {2023},
  doi = {10.1145/3600211.3604673}
}