In recent years the formerly quite strong interest in patient compliance has been questioned for being too paternalistic and oriented towards overly narrow biomedical goals as the basis for treatment recommendations. In line with this there has been a shift towards using the notion of adherence to signal an increased weight for patients’ preferences and autonomy in decision making around treatments. This ‘adherence-paradigm’ thus encompasses shared decision-making as an ideal and patient perspective and autonomy as guiding goals of care. What (...) this implies in terms of the importance that we have reason to attach to (non-)adherence and how has, however, not been explained. In this article, we explore the relationship between different forms of shared decision-making, patient autonomy and adherence. Distinguishing between dynamically and statically framed adherence we show how the version of shared decision-making advocated will have consequences for whether one should be interested in a dynamically or statically framed adherence and in what way patient adherence should be assessed. In contrast to the former compliance paradigm (where non-compliance was necessarily seen as a problem), using observations about (non-)adherence to assess the success of health care decision making and professional-patient interaction turns out to be a much less straightforward matter. (shrink)

I argue against the orthodox view of the relationship between propositional and doxastic justification. The view under criticism is: if p is propositionally justified for S in virtue of S's having reason R, and S believes p on the basis of R, then S's belief that p is doxastically justified. I then propose and evaluate alternative accounts of the relationship between propositional and doxastic justification, and conclude that we should explain propositional justification in terms of doxastic justification. If correct, this (...) proposal would constitute a significant advance in our understanding of the sources of epistemic justification. (shrink)

Reasons are given to justify the claim that computer simulations and computational science constitute a distinctively new set of scientific methods and that these methods introduce new issues in the philosophy of science. These issues are both epistemological and methodological in kind.

Evidentialism is a view about the conditions under which a person is epistemically justified in having a particular doxastic attitude toward a proposition. Evidentialism holds that the justified attitudes are determined entirely by the person's evidence. This is the traditional view of justification. It is now widely opposed. The essays included in this volume develop and defend the tradition.Evidentialism has many assets. In addition to providing an intuitively plausible account of epistemic justification, it helps to resolve the problem of the (...) criterion, helps to disentangle epistemic and ethical evaluations, and illuminates the relationship between epistemic evaluations of beliefs and the evaluation of the methods used to form beliefs. These issues are all addressed in the essays presented here. External world skepticism poses the classic problem for an epistemological theory. The final essay in this volume argues that evidentialism is uniquely well qualified to make sense of skepticism and to respond to its challenge.Evidentialism is a version of epistemic internalism. Recent epistemology has included many attacks on internalism and has seen the development of numerous externalist theories. The essays included here respond to those attacks and raise objections to externalist theories, especially the principal rival, reliabilism. Internalism generally has been criticized for having unacceptable deontological implications, for failing to connect epistemic justification to truth, and for failing to provide an adequate account of what makes basic beliefs justified. Each of these charges is answered in these essays. The collection includes two previously unpublished essays and new afterwords to five of the reprinted essays; it will be the definitive resource on evidentialism for all epistemologists. (shrink)

Truth is standardly considered a requirement on epistemic acceptability. But science and philosophy deploy models, idealizations and thought experiments that prescind from truth to achieve other cognitive ends. I argue that such felicitous falsehoods function as cognitively useful fictions. They are cognitively useful because they exemplify and afford epistemic access to features they share with the relevant facts. They are falsehoods in that they diverge from the facts. Nonetheless, they are true enough to serve their epistemic purposes. Theories that contain (...) them have testable consequences, hence are factually defeasible. (shrink)

Large language models (LLMs) such as ChatGPT or Google’s Bard have shown significant performance on a variety of text-based tasks, such as summarization, translation, and even the generation of new...

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

Fears of black-box algorithms are multiplying. Black-box algorithms are said to prevent accountability, make it harder to detect bias and so on. Some fears concern the epistemology of black-box algorithms in medicine and the ethical implications of that epistemology. Durán and Jongsma (2021) have recently sought to allay such fears. While some of their arguments are compelling, we still see reasons for fear.

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)

Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or (...) a rationale for particular decisions in individual cases, some commentators regard ceding medical decision‐making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious—as it often is in medicine—the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy. (shrink)

In patient-centred care, shared decision-making is advocated as the preferred form of medical decision-making. Shared decision-making is supported with reference to patient autonomy without abandoning the patient or giving up the possibility of influencing how the patient is benefited. It is, however, not transparent how shared decision-making is related to autonomy and, in effect, what support autonomy can give shared decision-making. In the article, different forms of shared decision-making are analysed in relation to five different aspects of autonomy: (1) self-realisation; (...) (2) preference satisfaction; (3) self-direction; (4) binary autonomy of the person; (5) gradual autonomy of the person. It is argued that both individually and jointly these aspects will support the models called shared rational deliberative patient choice and joint decision as the preferred versions from an autonomy perspective. Acknowledging that both of these models may fail, the professionally driven best interest compromise model is held out as a satisfactory second-best choice. (shrink)

Computer simulations are often claimed to be opaque and thus to lack transparency. But what exactly is the opacity of simulations? This paper aims to answer that question by proposing an explication of opacity. Such an explication is needed, I argue, because the pioneering definition of opacity by P. Humphreys and a recent elaboration by Durán and Formanek are too narrow. While it is true that simulations are opaque in that they include too many computations and thus cannot be checked (...) by hand, this doesn’t exhaust what we might want to call the opacity of simulations. I thus make a fresh start with the natural idea that the opacity of a method is its disposition to resist knowledge and understanding. I draw on recent work on understanding and elaborate the idea by a systematic investigation into what type of knowledge and what type of understanding are required if opacity is to be avoided and why the required sort of understanding, in particular, is difficult to achieve. My proposal is that a method is opaque to the degree that it’s difficult for humans to know and to understand why its outcomes arise. This proposal allows for a comparison between different methods regarding opacity. It further refers to a kind of epistemic access that is important in scientific work with simulations. (shrink)

Artificial intelligence (AI) is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system (...) creates both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making. (shrink)

Joseph Raz presents a penetrating exploration of the interdependence of value, reason, and the will. These essays illuminate a wide range of questions concerning fundamental aspects of human thought and action. Engaging Reason is a summation of many years of original, compelling, and influential work by a major contemporary philosopher.

The epistemic basing relation is the relation which must hold between a person's belief and the adequate reasons for holding that belief if the belief is to be epistemically justified by those reasons. Although the basing relation is a fundamental component of any adequate theory of epistemic justification, it has received scant attention in the literature. In this paper, I propose a novel causal analysis of the basing relation, one which helps to characterize an intemalist element which, I shall argue, (...) is required of any successful account of epistemic justification, and which confirms current trends away from coherentist and reliabilist theories of justification. (shrink)

There are distinct but legitimate notions of both personal justification and interpersonal justification. Interpersonal justification is definable in terms of personal justification. A connection is established between good argumentation and interpersonal justification.

Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate on the experimental role of computer simulations :483–496, 2009; Morrison in Philos Stud 143:33–57, 2009), the nature of computer data Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013; Humphreys, in: Durán, Arnold Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013), and the explanatory power of (...) computer simulations :277–292, 2008; Durán in Int Stud Philos Sci 31:27–45, 2017). The aim of this article is to show that these authors are right in assuming that results of computer simulations are to be trusted when computer simulations are reliable processes. After a short reconstruction of the problem of epistemic opacity, the article elaborates extensively on computational reliabilism, a specified form of process reliabilism with computer simulations located at the center. The article ends with a discussion of four sources for computational reliabilism, namely, verification and validation, robustness analysis for computer simulations, a history of successful implementations, and the role of expert knowledge in simulations. (shrink)

Clinical guidelines summarise available evidence on medical treatment, and provide recommendations about the most effective and cost-effective options for patients with a given condition. However, sometimes patients do not desire the best available treatment. Should doctors in a publicly-funded healthcare system ever provide sub-optimal medical treatment? On one view, it would be wrong to do so, since this would violate the ethical principle of beneficence, and predictably lead to harm for patients. It would also, potentially, be a misuse of finite (...) health resources. In this paper, we argue in favour of permitting sub-optimal choices on the basis of value pluralism, uncertainty, patient autonomy and responsibility. There are diverse views about how to evaluate treatment options, and patients’ right to self-determination and taking responsibility for their own lives should be respected. We introduce the concept of cost-equivalence, as a way of defining the boundaries of permissible pluralism in publicly-funded healthcare systems. As well as providing the most effective, available treatment for a given condition, publicly-funded healthcare systems should provide reasonable suboptimal medical treatments that are equivalent in cost to the optimal treatment. We identify four forms of cost-equivalence, and assess the implications of CE for decision-making. We evaluate and reject counterarguments to CE. Finally, we assess the relevance of CE for other treatment decisions including requests for potentially superior treatment. (shrink)