Reframing the responsibility gap in medical artificial intelligence: insights from causal selection and authorship attribution

The increasing use of AI in healthcare has sparked debates about responsibility and accountability for AI-related errors. The difficulty in attributing moral responsibility for undesirable outcomes caused by increasingly autonomous (often opaque) AI systems has become a new focal point in the debate on ‘responsibility gaps’. We approach the problem of these gaps by offering a framework that combines causal selection principles from the philosophy of science with recent accounts of authorship attribution in AI contexts. We argue this framework offers a more comprehensive and context-sensitive approach to the responsibility gap in medical AI.