{
  "abstract": "The advances in machine learning (ML)-based systems in medicine give rise to pressing epistemological and ethical questions. Clinical decisions are increasingly taken in highly digitised work environments, which we call artificial epistemic niches. By considering the case of ML systems in life-critical healthcare settings, we investigate (1) when users’ reliance on these systems can be characterised as epistemic dependence and (2) how this dependence turns into what we refer to as harmful epistemic dependence of clinical professionals on medical ML. The latter occurs when the impossibility of critically assessing the soundness of a system’s output in situ implies a moral obligation to comply with its recommendation since a failure to do so constitutes a moral risk that cannot be justified then and there. We analyse the epistemic and moral consequences of harmful epistemic dependence on the status of medical professionals. We conclude by assessing how a suitable design of the epistemic niche can address the problem.",
  "authors": [
    {
      "affiliations": [
        "Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands"
      ],
      "name": "Giorgia Pozzi"
    },
    {
      "affiliations": [
        "Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands"
      ],
      "name": "Stefan Buijsman"
    },
    {
      "affiliations": [
        "Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands"
      ],
      "name": "Jeroen van den Hoven"
    }
  ],
  "title": "Harmful epistemic dependence on medical machine learning and its moral implications",
  "uid": "dc181e69-8658-5a1d-99f9-55e172067ce4"
}