Work

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

Year: 2026

Type: article

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.

Source: ACM Journal on Responsible Computing

DOI: 10.1145/3802829

Authors: Diana Acosta-Navas, Henrik Kugelberg, Seth Lazar, Alan Rubel

Institutions: Loyola University Chicago, University of Warwick, Johns Hopkins University, University of Wisconsin–Madison

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W7150999938

FWCI:

Cites:

Cited by:

Related to: