This topical collection of Synthese is in honor of Catherine Z. Elgin. The idea for it arose in the context of an international book symposium dedicated to Elgin's latest book, organized by Katherine Dormandy, Christoph Jäger, and myself, which took place at the University of Innsbruck in March 2018. The topical collection comprises fourteen papers addressing a broad array of issues related to True Enough and to Elgin’s work more generally, plus a contribution by Elgin with detailed comments and replies. (...) True Enough is an extraordinarily rich, wide-ranging book; it reflects both the breadth and sharpness of Elgin’s philosophical gaze and exemplifies the impressive variety of her philosophical interests. In this introduction, I give an overview of the topical collection’s content, zooming in on the aspects of Elgin’s work which captured the contributors’ attention. My discussion will not remotely do justice to the complexity of Elgin’s system, but I hope that it will help the reader navigate the topical collection and appreciate how the fourteen papers relate to Elgin’s overall project. (shrink)

Developments in Machine Learning have attracted attention in a wide range of healthcare fields to improve medical practice and the benefit of patients. Particularly, this should be achieved by providing more or less automated decision recommendations to the treating physician. However, some hopes placed in ML for healthcare seem to be disappointed, at least in part, by a lack of transparency or traceability. Skepticism exists primarily in the fact that the physician, as the person responsible for diagnosis, therapy, and care, (...) has no or insufficient insight into how such recommendations are reached. The following paper aims to make understandable the specificity of the deliberative model of a physician-patient relationship that has been achieved over decades. By outlining the epistemic and inherently normative relationship between physicians and patients, I want to show how this relationship might be altered by non-traceable ML recommendations. With respect to some healthcare decisions, such changes in deliberative practice may create normatively far-reaching challenges. Therefore, in the future, a differentiation of decision-making situations in healthcare with respect to the necessary depth of insight into the process of outcome generation seems essential. (shrink)