The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that (...) computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence. To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care. (shrink)

Bioethics, Volume 36, Issue 2, Page 162-169, February 2022.

Biomedical research funding bodies across Europe and North America increasingly encourage—and, in some cases, require—investigators to involve members of the public in funded research. Yet there remains a striking lack of clarity about what ‘good’ or ‘successful’ public involvement looks like. In an effort to provide guidance to investigators and research organisations, representatives of several key research funding bodies in the UK recently came together to develop the National Standards for Public Involvement in Research. The Standards have critical implications for (...) the future of biomedical research in the UK and in other countries as researchers and funders abroad look to the Standards as a model for their own policy development. We assess the Standards and find that despite offering useful suggestions for dealing with practical challenges associated with public involvement, the Standards fail to address fundamental questions about when, why and with whom public involvement should be undertaken in the first place. We show that presented without this justificatory context, many of the recommendations in the Standards are, at best, fragments that require substantial elaboration by those looking to apply the Standards in their own work and, at worst, subject to potentially harmful misapplication by well-meaning investigators. As funding bodies increasingly push for public involvement in research, the key lesson of our analysis is that future recommendations about how public involvement should be conducted cannot be coherently formulated without a clear sense of the underlying goals and rationales for public involvement. (shrink)

We would like to thank the authors of the commentaries for their critical appraisal of our feature article, Who is afraid of black box algorithms?1 Their comments, suggestions and concerns are various, and we are glad that our article contributes to the academic debate about the ethical and epistemic conditions for medical Explanatory AI. We would like to bring to attention a few issues that are common worries across reviewers. Most prominently are the merits of computational reliabilism —in particular, when (...) promoted as an alternative to transparency—and CR as necessary but not sufficient for delivering trust. We finalise our response by addressing concerns about the place and role of artificial intelligence in medical decision-making and the physician’s responsibilities. We understand the concerns and reservations that some of the reviewers express regarding the epistemic merits of CR. We believe that, in part, this is due to a practice too deeply rooted in transparency. But on …. (shrink)

Dementia is highly prevalent and up until now, still incurable. If we may believe the narrative that is currently dominant in dementia research, in the future we will not have to suffer from dementia anymore, as there will be a simple techno-fix solution. It is just a matter of time before we can solve the growing public health problem of dementia. In this paper we take a critical stance towards overly positive narratives of techno-fixes by placing our empirical analysis of (...) dementia research protocols and political statements in a framework of technology assessment. From this perspective, it becomes obvious that a techno-fix is just one of many ways to approach societal problems and more importantly that technologies are way less perfect than they are presented. We will argue that this narrow scope, which focusses on the usual suspects for solving illnesses, reduces dementia to organismic aspects, and may be counterproductive in finding a cure for dementia. We conclude with outlining how the narrow scope can be balanced with other narratives and why we should have a reasonable scepticism towards the usual suspects. (shrink)

In their paper ‘Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts’, Kempt and Nagel discuss the use of medical AI systems and the resulting need for second opinions by human physicians, when physicians and AI disagree, which they call the rule of disagreement.1 The authors defend RoD based on three premises: First, they argue that in cases of disagreement in medical practice, there is an increased burden of proof for the physician in (...) charge, to defend why the opposing view is adopted or overridden. This burden for justification can be understood as an increased responsibility. In contrast, such burden does allegedly not arise, when physicians agree in their judgement. Second, in those medical contexts where humans collaborate with humans such justification can be provided, since human experts can discuss the evidence and reasons that have led them to their judgement, through which the sources of disagreement can be found and a justified decision can be made by the physician in charge. Third, unlike human-to-human collaboration, such communicative exchange is not possible with an AI system. Due to AI’s opacity, the physician in charge has no means of illuminating why the AI disagrees. Conclusively, the authors propose RoD as a solution. RoD suggests that a second human expert should be consulted for advise in cases of human–AI disagreement. Once AI systems become more widespread in clinical practice, it can be expected that such type of disagreement occurs more frequently. AI, after all, is being implemented, because it promises, among others, higher accuracy, which implies that some abnormalities will be detected that the physician would have missed.2 Hence, it is laudable to discuss the moral implications of …. (shrink)

Gene drive technologies (GDTs) have been proposed as a potential new way to alleviate the burden of malaria, yet have also raised ethical questions. A central ethical question regarding GDTs relates to whether it is morally permissible to intentionally modify or eradicate mosquitoes in this way and how the inherent worth of humans and non-human organisms should be factored into determining this. Existing analyses of this matter have thus far generally relied on anthropocentric and zoocentric perspectives and rejected an individualist (...) biocentric outlook in which all living organisms are taken to matter morally for their own sake. In this paper, we reconsider the implications of taking a biocentric approach and highlight nuances that may not be evident at first glance. First, we shortly discuss biocentric perspectives in general, and then outline Paul Taylor’s biocentric theory of respect for nature. Second, we explore how conflicting claims towards different organisms should be prioritised from this perspective and subsequently apply this to the context of malaria control using GDTs. Our ethical analysis shows that this context invokes the principle of self-defence, which could override the pro tanto concerns that a biocentrist would have against modifying malaria mosquitoes in this way if certain conditions are met. At the same time, the case study of GDTs underlines the relevance of previously posed questions and criticism regarding the internal consistency of Taylor’s egalitarian biocentrism. (shrink)