Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
Medical Artificial Intelligence (AI) is widely expected to transform clinical practice, yet the decision-making processes of many Machine Learning (ML) models remain opaque. Explainability has been advanced as a partial remedy to clarify why AI generates predictions, particularly in high-stakes contexts. Despite ongoing efforts, debates on what constitutes an adequate medical explanation remain unsettled. Yet, explanation has long been a central topic of inquiry in the philosophy of science and medicine. The insights developed in these fields, however, have been largely overlooked in contemporary explainable AI (XAI) research, leaving its foundational assumptions insufficiently examined. ...
| Authors | Martina Mattioli, Marcello Pelillo |
|---|---|
| Primary category | cs.AI |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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