Social Causes And Epistemic (in)Justice in Medical Machine Learning-Mediated Medical Practices

Abstract

The social aspects of causality in medicine and healthcare have been emphasized in recent debates in the philosophy of science as crucial factors that need to be considered to enable, among others, appropriate interventions in public health. Therefore, it seems central to recognize the bearing of social causes (broadly understood, e.g., social inequalities and socio-economic status) in bringing about certain concrete pathologies. Being aware of the relevance of social causes in medicine and healthcare is particularly important in the face of the role that artificial intelligence-based systems (such as machine learning algorithms) are increasingly playing in these high-stakes fields. In fact, these systems bear the dangerous potential of concealing relevant social causes. This is highly problematic not only because it reinforces issues of distributive injustice but also because it can pave the way for issues of epistemic injustice. The central aim of this chapter is to make a first effort to point out possible connections between the importance of recognizing social causes in medicine and healthcare and forms of epistemic injustice.

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