Towards the Political Philosophy of Computing: Conversations with Fairness and Machine Learning
The aim of this special section is to curate an interdisciplinary conversation between computer and information scientists and philosophers. The contributions explore how Fairness and Machine Learning: Limitations and Opportunities can be used as a springboard for examining the role of empirical computer science in the political philosophy of computing, and vice versa. Our hope is that this symposium can spark a multidisciplinary dialogue around questions that are at root philosophical but touch on technical aspects, the socio-technical systems in which ML is embedded and ML's social impacts. The aim of this special section is to curate an interdisciplinary conversation between computer and information scientists and philosophers. The contributions explore how Fairness and Machine Learning: Limitations and Opportunities can be used as a springboard for examining the role of empirical computer science in the political philosophy of computing, and vice versa. Our hope is that this symposium can spark a multidisciplinary dialogue around questions that are at root philosophical but touch on technical aspects, the socio-technical systems in which ML is embedded and ML's social impacts.
Authors
- Henrik D. Kugelberg (ORCID: https://orcid.org/0000-0001-5119-646X)
- Alan Rubel (ORCID: https://orcid.org/0000-0002-7028-7305)
- Diana Acosta-Navas
- Seth Lazar
Institutions
- University of Wisconsin–Madison (US)
- Johns Hopkins University (US)
- Loyola University Chicago (US)
- Johns Hopkins Medicine (US)
- University of Warwick (GB)
Publication Details
- Journal
- ACM Journal on Responsible Computing
- Published
- 2026-03-31
- DOI
- https://doi.org/10.1145/3802829
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00