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.

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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
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article

Towards the Political Philosophy of Computing: Conversations with Fairness and Machine Learning

Henrik D. Kugelberg, Alan Rubel, Diana Acosta-Navas, Seth Lazar
ACM Journal on Responsible Computing
Ethics and Social Impacts of AI
article

Towards the Political Philosophy of Computing: Conversations with Fairness and Machine Learning

Henrik D. Kugelberg, Alan Rubel, Diana Acosta-Navas, Seth Lazar
article en

Abstract

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.

ACM Journal on Responsible ComputingVol. 3(1)
University of Wisconsin–Madison (US), Johns Hopkins University (US), Loyola University Chicago (US), Johns Hopkins Medicine (US), University of Warwick (GB)
Openalex Percentile: Top 21%
Ethics and Social Impacts of AI
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