Abstract

In this paper, we discuss epistemic and ethical concerns brought about by machine learning (ML) systems implemented in medicine. We begin by fleshing out the logic underlying a common approach in the specialized literature (which we call the _informativeness account_). We maintain that the informativeness account limits its analysis to the impact of epistemological issues on ethical concerns without assessing the bearings that ethical features have on the epistemological evaluation of ML systems. We argue that according to this methodological approach, epistemological issues are _instrumental_ to and _autonomous_ of ethical considerations. This means that the informativeness account considers epistemological evaluation uninfluenced and unregulated by an ethical counterpart. Using an example that does not square well into the _informativeness account_, we argue for ethical assessments that have a substantial influence on the epistemological assessment of ML and that such influence should not be understood as merely informative but rather regulatory. Drawing on the case analyzed, we claim that within the theoretical framework of the informativeness approach, forms of epistemic injustice—especially _epistemic objectification_—remain unaddressed. Our analysis should motivate further research investigating the regulatory role that ethical elements play in the epistemology of ML.