This interdisciplinary symposium investigates fairness and machine learning's implications in political philosophy, emphasizing technical and 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. 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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Acosta-Navas et al. (2026) studied this question.
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