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.

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)

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)

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)

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)

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

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)

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)

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)

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)

Best known for his influential History of Greece, the historian and politician George Grote wrote this account of Plato's dialogues as a philosophical supplement to the History. First published in 1865 and written in dialogic form, Grote's account of Plato's works includes substantial footnotes and marginalia. This first volume focuses on Plato's early and transitional dialogues, all of which feature Socrates. It also includes a preface to the whole project which discusses the meaning and importance of philosophy itself, (...) and extensive introductory material on pre-Socratic philosophy, the life of Plato and history of the Platonic canon. With three volumes each running to over six hundred pages, Grote's scholarship is formidably comprehensive. The publication of Plato and the Other Companions of Sokrates confirmed him as one of the greatest authorities on Plato in the nineteenth century. (shrink)

In this book, T. M. Scanlon offers new answers to these questions, as they apply to the central part of morality that concerns what we owe to each other.

This volume brings together for the first time a significant number of Reid's manuscript papers on natural history, physiology and materialist metaphysics. An important contribution not only to Reid studies but also to our understanding of eighteenth-century science and its context.

Taking a medieval approach in content as well as in form—a compilation of lists—this volume creates a foundation for the study of the medieval mindset by establishing the terms and concepts that scholars would have had in a common at the time: an invaluable lingua franca. With a pedagogical appeal, this interdisciplinary book is a combination text, reference, and popular work that provides a fascinating intellectual history of the Middle Ages while complimenting the study of other works from that period.

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

Broad in its geographic scope and yet grounded in original archival research, this book situates the inception of modern aesthetic theory – the philosophical analysis of art and beauty - in theological contexts that are crucial to explaining why it arose. Simon Grote presents seminal aesthetic theories of the German and Scottish Enlightenments as outgrowths of a quintessentially Enlightenment project: the search for a natural 'foundation of morality' and a means of helping naturally self-interested human beings transcend their own (...) self-interest. This conclusion represents an important alternative to the standard history of aesthetics as a series of preludes to the achievements of Immanuel Kant, as well as a reinterpretation of several canonical figures in the German and Scottish Enlightenments. It also offers a foundation for a transnational history of the Enlightenment without the French philosophes at its centre, while solidly endorsing historians' growing reluctance to call the Enlightenment a secularising movement. (shrink)

Thomas Reid was a philosopher who founded the Scottish school of 'common sense'. Much of Reid's work is a critique of his contemporary, David Hume, whose empiricism he rejects. In this work, written after Reid's appointment to a professorship at the university of Glasgow, and published in 1785, he turns his attention to ideas about perception, memory, conception, abstraction, judgement, reasoning and taste. He examines the work of his predecessors and contemporaries, arguing that 'when we find philosophers maintaining that (...) there is no heat in the fire, nor colour in the rainbow … we may be apt to think the whole to be only a dream of fanciful men, who have entangled themselves in cobwebs spun out of their own brain'. Written by one of the Scottish Enlightenment's most important thinkers, this work brings to life the intellectual debates of the time. (shrink)

Thomas Aquinas produced a voluminous body of work on moral theory, and much of that work is on virtue, particularly the status and value of the virtues as principles of virtuous acts, and the way in which a moral life can be organized around them schematically. Thomas Osborne presents Aquinas's account of virtue in its historical, philosophical and theological contexts, to show the reader what Aquinas himself wished to teach about virtue. His discussion makes the complexities of Aquinas's (...) moral thought accessible to readers despite the differences between Thomas's texts themselves, and the distance between our background assumptions and his. The book will be valuable for scholars and students in ethics, medieval philosophy, and theology. (shrink)

This essay considers how scholarly approaches to the development of molecular biology have too often narrowed the historical aperture to genes, overlooking the ways in which other objects and processes contributed to the molecularization of life. From structural and dynamic studies of biomolecules to cellular membranes and organelles to metabolism and nutrition, new work by historians, philosophers, and STS scholars of the life sciences has revitalized older issues, such as the relationship of life to matter, or of physicochemical inquiries to (...) biology. This scholarship points to a novel molecular vista that opens up a pluralist view of molecularizations in the twentieth century and considers their relevance to current science. (shrink)

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

Best known for his influential History of Greece, the historian and politician George Grote wrote this account of Plato's dialogues as a philosophical supplement to the History. First published in 1865 and written in dialogic form, Grote's account of Plato's works includes substantial footnotes and marginalia. This first volume focuses on Plato's early and transitional dialogues, all of which feature Socrates. It also includes a preface to the whole project which discusses the meaning and importance of philosophy itself, (...) and extensive introductory material on pre-Socratic philosophy, the life of Plato and history of the Platonic canon. With three volumes each running to over six hundred pages, Grote's scholarship is formidably comprehensive. The publication of Plato and the Other Companions of Sokrates confirmed him as one of the greatest authorities on Plato in the nineteenth century. (shrink)

The term ‘cell’, in addition to designating fundamental units of life, has also been applied since the nineteenth century to technical apparatuses such as fuel and galvanic cells. This paper shows that such technologies, based on the electrical effects of chemical reactions taking place in containers, had a far-reaching impact on the concept of the biological cell. My argument revolves around the controversy over oxidative phosphorylation in bioenergetics between 1961 and 1977. In this scientific conflict, a two-level mingling of technological (...) culture, physical chemistry and biological research can be observed. First, Peter Mitchell explained the chemiosmotic hypothesis of energy generation by representing cellular membrane processes via an analogy to fuel cells. Second, in the associated experimental scrutiny of membranes, material cell models were devised that reassembled spatialized molecular processes in vitro. Cells were thus modelled both on paper and in the test tube not as morphological structures but as compartments able to perform physicochemical work. The story of cells and membranes in bioenergetics points out the role that theories and practices in physical chemistry had in the molecularization of life. These approaches model the cell as a ‘topology of molecular action’, as I will call it, and it involves concepts of spaces, surfaces and movements. They epitomize an engineer’s vision of the organism that has influenced diverse fields in today’s life sciences. (shrink)

Berichte zur Wissenschaftsgeschichte, EarlyView.

There has been an explosion of interest in the metaphysics of fundamentality in recent decades. The consensus view, called metaphysical foundationalism, maintains that there is something absolutely fundamental in reality upon which everything else depends. However, a number of thinkers have chal- lenged the arguments in favor of foundationalism and have proposed competing non-foundationalist ontologies. This paper provides a systematic and critical introduction to metaphysical foundationalism in the current literature and argues that its relation to ontological dependence and substance should (...) be qualified in important ways. (shrink)

In a recent paper Weinberg (2007) claims that there is an essential mark of trustworthiness which typical sources of evidence as perception or memory have, but philosophical intuitions lack, namely that we are able to detect and correct errors produced by these “hopeful” sources. In my paper I will argue that being a hopeful source isn't necessary for providing us with evidence. I then will show that, given some plausible background assumptions, intuitions at least come close to being hopeful, if (...) they are reliable. If this is true, Weinberg's new challenge comes down to the claim that philosophical intuitions are not reliable since they are significantly unstable. In the second part of my paper I will argue that and why the experimentally established instability of folk intuitions about philosophical cases does not show that philosopher's expert intuitions about these cases are instable. (shrink)

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...

Feeling good: four ways -- Finding that feeling -- The place of pleasure -- Knowing what's what -- Making things happen -- Being good -- Love and friendship -- Putting it together.

Looking back on it, it seems almost incredible that so many equally educated, equally sincere compatriots and contemporaries, all drawing from the same limited stock of evidence, should have reached so many totally different conclusions---and always with complete certainty.