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)

In recent years, there has been a surge of high-profile publications on applications of artificial intelligence (AI) systems for medical diagnosis and prognosis. While AI provides various opportunities for medical practice, there is an emerging consensus that the existing studies show considerable deficits and are unable to establish the clinical benefit of AI systems. Hence, the view that the clinical benefit of AI systems needs to be studied in clinical trials—particularly randomised controlled trials (RCTs)—is gaining ground. However, an issue that (...) has been overlooked so far in the debate is that, compared with drug RCTs, AI RCTs require methodological adjustments, which entail ethical challenges. This paper sets out to develop a systematic account of the ethics of AI RCTs by focusing on the moral principles of clinical equipoise, informed consent and fairness. This way, the objective is to animate further debate on the (research) ethics of medical AI. (shrink)

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. (...) In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.

In light of recent advances in machine learning for medical applications, the automation of medical diagnostics is imminent. That said, before machine learning algorithms find their way into clinical practice, various problems at the epistemic level need to be overcome. In this paper, we discuss different sources of uncertainty arising for clinicians trying to evaluate the trustworthiness of algorithmic evidence when making diagnostic judgments. Thereby, we examine many of the limitations of current machine learning algorithms (with deep learning in particular) (...) and highlight their relevance for medical diagnostics. Among the problems we inspect are the theoretical foundations of deep learning (which are not yet adequately understood), the opacity of algorithmic decisions, and the vulnerabilities of machine learning models, as well as concerns regarding the quality of medical data used to train the models. Building on this, we discuss different desiderata for an uncertainty amelioration strategy that ensures that the integration of machine learning into clinical settings proves to be medically beneficial in a meaningful way. (shrink)

The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what exactly they mean by robustness. In this paper, we provide a conceptual analysis of the term robustness, with the aim to develop a common language, that allows us to weave together different strands of robustness research. We define robustness as (...) the relative stability of a robustness target with respect to specific interventions on a modifier. Our account captures the various sub-types of robustness that are discussed in the research literature, including robustness to distribution shifts, prediction robustness, or the robustness of algorithmic explanations. Finally, we delineate robustness from adjacent key concepts in ML, such as extrapolation, generalization, and uncertainty, and establish it as an independent epistemic concept. (shrink)

Various publications claim that medical AI systems perform as well, or better, than clinical experts. However, there have been very few controlled trials and the quality of existing studies has been called into question. There is growing concern that existing studies overestimate the clinical benefits of AI systems. This has led to calls for more, and higher-quality, randomized controlled trials of medical AI systems. While this a welcome development, AI RCTs raise novel methodological challenges that have seen little discussion. We (...) discuss some of the challenges arising in the context of AI RCTs and make some suggestions for how to meet them. (shrink)

Bioethics, Volume 36, Issue 2, Page 134-142, February 2022.

This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the grounds for many ethical problems that currently claim centre-stage in the bioethical debate. Accordingly, this paper argues that the best way (...) to make progress in remedying these ethical problems is to distil their epistemic core and to identify appropriate epistemic norms as guardrails. The viability of this approach will be highlighted based on four key issues: trust, responsibility, paternalism, and fairness. In that respect, the paper also contributes to a more pronounced view of the specific methodological challenges for ethical theorizing, when the point of reference are complex statistical models that make predictions, rather than individual agents, acting for reasons. (shrink)

There is much to learn from Durán and Jongsma’s paper.1 One particularly important insight concerns the relationship between epistemology and ethics in medical artificial intelligence. In clinical environments, the task of AI systems is to provide risk estimates or diagnostic decisions, which then need to be weighed by physicians. Hence, while the implementation of AI systems might give rise to ethical issues—for example, overtreatment, defensive medicine or paternalism2—the issue that lies at the heart is an epistemic problem: how can physicians (...) know whether to trust decisions made by AI systems? In this manner, various studies examining the interaction of AI systems and physicians have shown that without being able to evaluate their trustworthiness, especially novice physicians become over-reliant on algorithmic support—and ultimately are led astray by incorrect decisions.3–5 This leads to a second insight from the paper, namely that even if some AI system happens to be opaque, it is still not built on the moon. To assess its trustworthiness, AI developers or physicians have different sorts of higher order evidence at hand. Most importantly, …. (shrink)

The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algorithmic decisions unfair relative to the standard fairness metrics in machine learning. In this paper, we defend the permissible use of affirmative algorithms; (...) that is, algorithms trained on diverse datasets that perform better for traditionally disadvantaged groups. Whilst such algorithmic decisions may be unfair, the fairness of algorithmic decisions is not the appropriate locus of moral evaluation. What matters is the fairness of final decisions, such as diagnoses, resulting from collaboration between clinicians and algorithms. We argue that affirmative algorithms can permissibly be deployed provided the resultant final decisions are fair. (shrink)

After a wave of breakthroughs in image‐based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. However, prominent researchers are claiming that another paradigm shift in medical ML is imminent—due to most recent staggering successes of large language models—from single‐purpose applications toward generalist models, driven by natural language. This article investigates the implications of this paradigm shift for the ethical debate. Focusing on issues like trust, transparency, threats of patient autonomy, responsibility issues in the (...) collaboration of clinicians and ML models, fairness, and privacy, it will be argued that the main problems will be continuous with the current debate. However, due to functioning of large language models, the complexity of all these problems increases. In addition, the article discusses some profound challenges for the clinical evaluation of large language models and threats to the reproducibility and replicability of studies about large language models in medicine due to corporate interests. (shrink)

This paper develops an account of the opacity problem in medical machine learning (ML). Guided by pragmatist assumptions, I argue that opacity in ML models is problematic insofar as it potentially undermines the achievement of two key purposes: ensuring generalizability and optimizing clinician–machine decision-making. Three opacity amelioration strategies are examined, with explainable artificial intelligence (XAI) as the predominant approach, challenged by two revisionary strategies in the form of reliabilism and the interpretability by design. Comparing the three strategies, I argue that (...) interpretability by design is most promising to overcome opacity in medical ML. Looking beyond the individual opacity amelioration strategies, the paper also contributes to a deeper understanding of the problem space and the solution space regarding opacity in medical ML. (shrink)

This paper argues that machine learning and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal knowledge is necessary for underwriting predictions about the outcomes of public health interventions. (...) While there is great plausibility to the idea that it is, conviction that something is impossible does not by itself motivate a constraint to forbid trying. We disambiguate the possible motivations for such a constraint into definitional, metaphysical, epistemological, and pragmatic considerations and argue that “Proceed with caution” is the outcome of each. We then argue that there are positive reasons to proceed, albeit cautiously. Causal inference enforces existing classification schema prior to the testing of associational claims, but associations and classification schema are more plausibly discovered in a back-and-forth process of gaining reflective equilibrium. ML instantiates this kind of process, we argue, and thus offers the welcome prospect of uncovering meaningful new concepts in epidemiology and public health—provided it is not causally constrained. (shrink)

Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful – at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap. Comparing two social science case studies to a paradigm example from the natural sciences, we argue that, (...) in addition to explanation, prediction is an important goal of social science – and we identify constraints that impede pure ML prediction from being successful in that field. As a remedy, we outline elements of an integrative modelling approach that combines explanatory models and predictive ML models. (shrink)

Er zijn twee domeinen in de traditie van het Europese humanisme, dat van de democratie en dat van kunst, literatuur, filosofie en dergelijke. Alexis de Tocqueville geeft in zijn boek over de democratie in Amerika middelen aan om degeneratie van de democratie tot een zacht democratisch despotisme te voorkomen. Tot die middelen behoren bestuurlijke decentralisatie en het invoeren van kleinschalige organisatievormen. Een ander middel zijn bijvoorbeeld nationale debatten en het activeren van een partijsysteem ten tijde van grote nationale bedreigingen. (...) Het is van belang naast werk en spel ruimte te scheppen voor vrije tijd, tijd die vrij is voor kunst, literatuur, filosofie en dergelijke. (shrink)

Volume: 4 Publisher: London J. Murray Publication date: 1888 Subjects: Plato Socrates Philosophy, Ancient Notes: This is an OCR reprint.

v. 1. Editorial introduction -- v. 2. The English and Latin texts (i) -- v. 3. The English and Latin texts (ii).

The newly updated Right and Wrong 2nd Edition is an accessible introduction to the major traditions in western philosophical ethics, written in a lively and engaging style. It is designed for entry-level ethics courses and includes real-life ethical scenarios chosen to appeal directly to students. Greatly expanded and improved, this successful text introduces students to the major ethical traditions, and provides a simple methodology for resolving ethical dilemmas Treats teleological and deontological approaches to ethics as the two most important traditions, (...) but now includes chapters on virtue ethics and the ethics of care The very accessible writing style speaks directly to students’ own experience Draws examples from three types of real-life ethical scenarios submitted by students: academic dishonesty, partying, and personal relationships Provides a concise treatment of this notoriously complex subject, perfect for entry-level ethics and applied ethics courses. (shrink)

When making decisions, humans can observe many kinds of information about others' activities, but their effects on performance are not well understood. We investigated social learning strategies using a simple problem-solving task in which participants search a complex space, and each can view and imitate others' solutions. Results showed that participants combined multiple sources of information to guide learning, including payoffs of peers' solutions, popularity of solution elements among peers, similarity of peers' solutions to their own, and relative payoffs from (...) individual exploration. Furthermore, performance was positively associated with imitation rates at both the individual and group levels. When peers' payoffs were hidden, popularity and similarity biases reversed, participants searched more broadly and randomly, and both quality and equity of exploration suffered. We conclude that when peers' solutions can be effectively compared, imitation does not simply permit scrounging, but it can also facilitate propagation of good solutions for further cumulative exploration. (shrink)

Berichte zur Wissenschaftsgeschichte, EarlyView.

It is somewhat misleading to think of the Franciscans as forming a “school” in ethics, since there was a fair bit of diversity among Franciscans. Nonetheless, one can identify certain characteristic tendencies of Franciscan moral thought, and certain “celebrity” Franciscans whose views in ethics and moral psychology are particularly noteworthy. I shall first offer an overview of the general character of Franciscan moral thought in the late thirteenth and early fourteenth centuries and then turn to a more detailed examination of (...) the thought of John Duns Scotus (1265/66-1308) and William Ockham (c. 1288-1347) on three central matters of debate: the nature of the virtues, the relationship between intellect and will, and the relationship between moral requirements and the divine will. (shrink)

Microbial diversity has become a leitmotiv of contemporary microbiology, as epitomized in the concept of the microbiome, with significant consequences for the classification of microbes. In this paper, I contrast microbiology’s current diversity ideal with its influential predecessor in the twentieth century, that of purity, as epitomized in Robert Koch’s bacteriological culture methods. Purity and diversity, the two polar opposites with regard to making sense of the microbial world, have been operationalized in microbiological practice by tools such as the “clean” (...) Petri dish versus the “dirty” Winogradsky column, the latter a container that mimics, in the laboratory, the natural environment that teems with diverse microbial life. By tracing the impact of the practices and concepts of purity and diversity on microbial classification through a history of techniques, tools, and manuals, I show the shifts in these concepts over the last century. Juxtaposing the dominant purity ideal with the more restricted, but continuously articulated, diversity ideal in microbial ecology not only provides a fresh perspective on microbial classification that goes beyond its intellectual history, but also contextualizes the present focus on diversity. By covering the period of a century, this paper outlines a revised longue durée historiography that takes its inspiration from artifacts, such as Petri dish and the Winogradsky column, and thereby simple, but influential technologies that often remain invisible. This enables the problem of historical continuity in modern science to be addressed and the accelerationist narratives of its development to be countered. (shrink)

Microbial diversity has become a leitmotiv of contemporary microbiology, as epitomized in the concept of the microbiome, with significant consequences for the classification of microbes. In this paper, I contrast microbiology’s current diversity ideal with its influential predecessor in the twentieth century, that of purity, as epitomized in Robert Koch’s bacteriological culture methods. Purity and diversity, the two polar opposites with regard to making sense of the microbial world, have been operationalized in microbiological practice by tools such as the “clean” (...) Petri dish versus the “dirty” Winogradsky column, the latter a container that mimics, in the laboratory, the natural environment that teems with diverse microbial life. By tracing the impact of the practices and concepts of purity and diversity on microbial classification through a history of techniques, tools, and manuals, I show the shifts in these concepts over the last century. Juxtaposing the dominant purity ideal with the more restricted, but continuously articulated, diversity ideal in microbial ecology not only provides a fresh perspective on microbial classification that goes beyond its intellectual history, but also contextualizes the present focus on diversity. By covering the period of a century, this paper outlines a revised longue durée historiography that takes its inspiration from artifacts, such as Petri dish and the Winogradsky column, and thereby simple, but influential technologies that often remain invisible. This enables the problem of historical continuity in modern science to be addressed and the accelerationist narratives of its development to be countered. (shrink)

In this book Thomas Wren uncovers and assesses the largely hidden philosophical assumptions about human motivation that have shaped contemporary psychological ...

Among all the isms worthy of examination in any study of eighteenth-century German philosophy, Wolffianism is undoubtedly among the worthiest. Broadly defined as adherence to teachings of Christian...

INTRODUCTION Although the writings of Thomas Reid are very fertile and interesting, his life is biographically barren in comparison to such seventeenth - and ...

We invite systematic consideration of the metaphors of cycles and circulation as a long-term theme in the history of the life and environmental sciences and medicine. Ubiquitous in ancient religious and philosophical traditions, especially in representing the seasons and the motions of celestial bodies, circles once symbolized perfection. Over the centuries cyclic images in western medicine, natural philosophy, natural history and eventually biology gained independence from cosmology and theology and came to depend less on strictly circular forms. As potent ‘canonical (...) icons’, cycles also interacted with representations of linear and irreversible change, including arrows, arcs, scales, series and trees, as in theories of the Earth and of evolution. In modern times life cycles and reproductive cycles have often been held to characterize life, in some cases especially female life, while human efforts selectively to foster and disrupt these cycles have harnessed their productivity in medicine and agriculture. But strong cyclic metaphors have continued to link physiology and climatology, medicine and economics, and biology and manufacturing, notably through the relations between land, food and population. From the grand nineteenth-century transformations of matter to systems ecology, the circulation of molecules through organic and inorganic compartments has posed the problem of maintaining identity in the face of flux and highlights the seductive ability of cyclic schemes to imply closure where no original state was in fact restored. More concerted attention to cycles and circulation will enrich analyses of the power of metaphors to naturalize understandings of life and their shaping by practical interests and political imaginations. (shrink)

THE NATURE OF ART is a collection of 29 seminal, historically-organized readings that are focused on a basic philosophical question: What is Art? Including writings from the Western tradition'both Continental and Analytic traditions'as well as non-Western, minority, and feminist writings, this volume provides students with a rich set of resources to explore this matter both broadly and deeply. Introductions to each reading situate the selection amidst each respective thinker's body of work and the greater philosophical context in which the remarks (...) arose. Reading questions accompany each selection, drawing students' attention to key points to be encountered. Hailed by reviewers and adopters for its clarity and rigor, Wartenberg's THE NATURE OF ART offers a lively and engaging introduction to the philosophy of art. (shrink)

For courses in Business Ethics A fresh approach to the assumptions that underlie business practices Two recent events — the 2008 economic meltdown and the ongoing concentration of the nation's wealth in the hands of a very small percentage of the population — have led many people to question a number of basic assumptions about business, corporations, and the workings of contemporary free-market capitalism in a global economy. Written as a dialogue between Socrates and a hypothetical contemporary CEO,Socrates Comes to (...) Wall Street leads students to think critically about perspectives and practices that are taken as “givens” in most American business schools and corporations. Employing this original and provocative approach, author Thomas White seeks to encourage the next generation of business leaders to be more astute about the implications of their actions, and to act more prudently and more fairly than we have seen in the recent past. (shrink)

This chapter contains section titled: Personhood: A Start Are Dolphins Persons? Language and the Hand Personhood Redefined Conclusion: What Kind of Beings Are Dolphins?

Een van de hoekstenen van het idee dat de wetenschap een eenheid is, wordt gevormd door de overtuiging dat er in haar rijk één taal wordt gesproken: de taal van de feiten. Natuurlijk zijn er tussen de wetenschappen ook grote verschillen, maar die betreffen volgens deze overtuiging het gebied van de theorieën. De data waarop deze zich baseren, zouden neutraal geformuleerd kunnen worden. Als dit niet zo zou zijn, wordt ons het angstbeeld van een grote spraakverwarring voorgehouden.

Big Ideas for Little Kids includes everything a teacher, a parent, or a college student needs to teach philosophy to elementary school children from picture books. Written in a clear and accessible style, the book explains why it is important to allow young children access to philosophy during primary-school education. Wartenberg also gives advice on how to construct a "learner-centered" classroom, in which children discuss philosophical issues with one another as they respond to open-ended questions by saying whether they agree (...) or disagree with what others have said. (shrink)

Some theories of goodness are descriptively rich: they have much to say about what makes things good. Neo-Aristotelian accounts, for instance, detail the various features that make a human being, a dog, a bee good relative to facts about those forms of life. Famously, such theories of relative goodness tend to be comparatively poor: they have little or nothing to say about what makes one kind of being better than another kind. Other theories of goodness—those that take there to be (...) absolute goodness—are comparatively rich: they offer grounds for judging some types of things better than others because they have more absolute goodness. Moorean accounts, for example, can tell us that humans and human experiences are superior to bees and blades of grass. But such theories tend to be descriptively poor: they struggle to tell us in virtue of what this is so. In this essay we motivate and flesh out a view that splits the difference between accounts of goodness as relative and accounts of goodness as absolute. Such a view holds promise only if the mechanics of this kind of metaphysics of goodness can be worked out. Here we present a view on which the paradigm for absolute goodness is God and the paradigm for each kind of relative goodness is a divine idea. (shrink)

Proceedings of a conference held in Apr. 2008 in Washington, D.C.

Father Williams explains how the conscience is formed through our training and experiences and informed by the Holy Spirit, making it an essential tool for daily living. He uses familiar and surprising characters to illustrate the positive choices conscience can direct--and the disaster that results when a conscience is undeveloped or ignored. Questions he tackles include "Is it more important to be smart or good?""Is there a morally right thing to do in every situation?" and "Is the Christian moral life (...) an exciting adventure, or a necessary burden?" Rich, provocative, and practical for everyday decision making, KNOWING RIGHT FROM WRONG is a must-read for all who hunger for personal holiness. (shrink)