This article examines the role of medical doctors, AI designers, and other stakeholders in making applied AI and machine learning ethically acceptable on the general premises of shared decision-making in medicine. Recent policy documents such as the EU strategy on trustworthy AI and the research literature have often suggested that AI could be made ethically acceptable by increased collaboration between developers and other stakeholders. The article articulates and examines four central alternative models of how AI can be designed and applied (...) in patient care, which we call the ordinary evidence model, the ethical design model, the collaborative model, and the public deliberation model. We argue that the collaborative model is the most promising for covering most AI technology, while the public deliberation model is called for when the technology is recognized as fundamentally transforming the conditions for ethical shared decision-making. (shrink)

Currently, many women who are expecting to give birth have no option but to attempt vaginal delivery, since access to elective planned caesarean sections (PCS) in the absence of what is deemed to constitute ‘clinical need’ is variable. In this paper, we argue that PCS should be routinely offered to women who are expecting to give birth, and that the risks and benefits of PCS as compared with planned vaginal delivery should be discussed with them. Currently, discussions of elective PCS (...) arise in the context of what are called ‘Maternal Request Caesarean Sections’ (MRCS) and there is a good deal of support for the position that women who request PCS without clinical indication should be provided with them. Our argument goes further than support for acceding to requests for MRCS: we submit that healthcare practitioners caring for women with uncomplicated pregnancies have a positive duty to inform them of the option of PCS as opposed to assuming vaginal delivery as a default, and to provide (or arrange for the provision of) PCS if that is the woman's preferred manner of delivery. (shrink)