In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical (...) diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare. (shrink)

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation (...) as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science. (shrink)

Techno-colonialism, which I argue here to specifically mean the transfer of technology and its values and norms from one locale to another, has become a serious concern with the advancement of socially disruptive technologies1 of the Fourth Industrial Revolution (4IR), like artificial intelligence and robots. While the transfer of technology from one locale, especially economically advanced countries, to developing countries comes with economic benefits for both regions, it is important to understand that technologies are not value- neutral; they come with (...) the values, cultures, and worldviews of their designers. However, despite the nonvalue-neutrality of the technologies of the 4IR, they are still relevant for subSaharan Africa’s development. Thus, using a phenomenological approach, especially the sub-Saharan African experiences of past histories of colonialism, I prescribe cautionary measures that sub-Saharan Africans ought to take in approaching the current industrial revolution and its technologies. (shrink)

How should we define ‘health’ and ‘disease’? There are three main positions in the literature. Naturalists desire value-free definitions based on scientific theories. Normativists believe that our uses of ‘health’ and ‘disease’ reflect value judgments. Hybrid theorists offer definitions containing both normativist and naturalist elements. This paper discusses the problems with these views and offers an alternative approach to the debate over ‘health’ and ‘disease’. Instead of trying to find the correct definitions of ‘health’ and ‘disease’ we should explicitly talk (...) about the considerations that are central in medical discussions, namely state descriptions and normative claims . This distinction avoids the problems facing the major approaches to defining ‘health’ and ‘disease’, and it more clearly captures what matters in medical discussions. (shrink)