This paper examines racial discrimination and algorithmic bias in predictive policing algorithms (PPAs), an emerging technology designed to predict threats and suggest solutions in law enforcement. We first describe what discrimination is in a case study of Chicago’s PPA. We then explain their causes with Broadbent’s contrastive model of causation and causal diagrams. Based on the cognitive science literature, we also explain why fairness is not an objective truth discoverable in laboratories but has context-sensitive social meanings that need to be (...) negotiated through democratic processes. With the above analysis, we next predict why some recommendations given in the bias reduction literature are not as effective as expected. Unlike the cliché highlighting equal participation for all stakeholders in predictive policing, we emphasize power structures to avoid hermeneutical lacunae. Finally, we aim to control PPA discrimination by proposing a governance solution—a framework of a social safety net. (shrink)

In this review study, I reflect on Richard Horton’s book and his thesis that Western countries failed in their response to the current epidemic in the first half of 2020, with a few exceptions. In the five sections of the paper, after an initial modification of Horton’s thesis (A), I discuss briefly: the suppression approach in China (B), the mitigation approach in the West (C), the SARS epidemic as the key global public health event (D), the causes of Western failure (...) (epistemic, political, ethical, cultural, etc.) (E), and final envoi (F). (shrink)

In this paper, we use the case of the COVID-19 pandemic in Europe to address the question of what kind of knowledge we should incorporate into public health policy. We show that policy-making during the COVID-19 pandemic has been biomedicine-centric in that its evidential basis marginalised input from non-biomedical disciplines. We then argue that in particular the social sciences could contribute essential expertise and evidence to public health policy in times of biomedical emergencies and that we should thus strive for (...) a tighter integration of the social sciences in future evidence-based policy-making. This demand faces challenges on different levels, which we identify and discuss as potential inhibitors for a more pluralistic evidential basis. (shrink)

What kind of a thing are chronic diseases? Are they objects, bundles of signs and symptoms, properties, processes, or fictions? Rather than using concept analysis—the standard approach to disease in the philosophy of medicine—to answer this metaphysical question, I use a bottom-up, inductive approach. I argue that chronic diseases are bodily states or properties—often dispositional, but sometimes categorical. I also investigate the nature of related pathological entities: pathogenesis, etiology, and signs and symptoms. Finally, I defend my view against alternate accounts (...) of the nature of disease. (shrink)

Identifying the necessary and sufficient conditions for individuating and classifying diseases is a matter of great importance in the fields of law, ethics, epidemiology, and of course, medicine. In this paper, I first propose a means of achieving this goal, ensuring that no two distinct disease-types could correctly be ascribed to the same disease-token. I then posit a metaphysical ontology of diseases—that is, I give an account of what a disease is. This is essential to providing the most effective means (...) of interfering with disease processes. Following existing work in the philosophy of medicine and epidemiology (primarily Christopher Boorse; Caroline Whitbeck; Alexander Broadbent), philosophy of biology (Joseph LaPorte; D.L. Hull), conditional analyses of causation (J.L. Mackie; David Lewis), and recent literature on dispositional essentialism (Stephen Mumford and Rani Anjum; Alexander Bird), I endorse a dispositional conception of disease. Following discussion of various conceptions of disease-identity, their relations to the clinical and pathological effects of the diseases in question, and how diseases are treated, I conclude (i) that diseases should be individuated by their causes, and (ii) that diseases are causal processes best seen as simultaneously acting sequences of mutually manifesting dispositions. (shrink)

Christopher Boorse’s Health care ethics: an introduction, Temple University Press, Philadelphia, pp 359–393, 1987; in Humber, Almeder, Totowa What is disease?, Humana Press, New York City, pp 1–134, 1997; J Med Philos, 39:683–724, 2014) Bio-Statistical Theory comprehends diseases in terms of departures from natural norms, which involve an objectively describable deviation from the proper physiological or psychological functioning of parts of the human organism. I argue that while recent revisions and additional considerations shield the BST from a number of issues (...) raised by critics, they give rise to significant new challenges. These are related to the attribution of epistemic authority, the possibility of multiple concepts in the authoritative literature, and the framing of BST as “philosophical explication.” The conclusion is that, in its current form, the BST is unable to provide a robust framework for a naturalist classification. (shrink)

This article examines the relationship between the precautionary principle and the well-known Hill criteria of causation. Some have charged that the Hill criteria are anti-precautionary because the...

We are pleased to once again present to the readers of Theoretical Medicine and Bioethics papers from the Philosophy of Medicine Roundtable. Previous issues have followed the 3rd and 4th Roundtables, and the current issue presents a selection from the more than 20 papers presented at the 5th Philosophy of Medicine Roundtable, which took place in New York, at Columbia University, in November 2013. Like its predecessors, held in Birmingham, AL, Rotterdam, and San Sebastian, this Roundtable attracted speakers from around (...) the world. It also featured keynote presentations from Rita Charon of Columbia University and Ross Upshur of the University of Toronto.It may seem somewhat odd to feature a special issue on philosophy of medicine in a journal that effectively has philosophy of medicine in its title. However, a review of the contents of this journal and similar ones, such as the Journal of Medicine and Philosophy, will quickly reveal such an issue’s purpose. The dominant content of these j .. (shrink)

This paper primarily argues that Epidemiology is Ecosystem Science. It will not only explore this notion in detail but will also relate it to the argument that Classical Chinese Medicine was/is Ecosystem Science. Ecosystem Science and Ecosystem Science share these characteristics: they do not subscribe to the monogenic conception of disease; they involve multi variables; the model of causality presupposed is multi-factorial as well as non-linear.

In this paper, I utilise the tools of analytic philosophy to amalgamate mono-causal and multi-causal theories of disease. My aim is to better integrate viral and socio-economic explanations of AIDS in particular, and to consider how the perceived divide between mono-causal and multi-causal theories played a role in the tragedy of AIDS denialism in South Africa in the early 2000s. Currently, there is conceptual ambiguity surrounding the relationship between mono-causal and multi-causal theories in biomedicine and epidemiology. Mono-causal theories focus on (...) single, typically microbial, sources of illness and are most concerned with infectious diseases. By contrast, multi-causal theories allow for multiple factors to underpin a disease’s aetiology, including socio-economic and behavioural factors, and they usually focus on chronic non-communicable diseases. However, if these theories are taken to be strictly distinct, this prevents the inclusion of both microbial and socio-economic factors in a single explanation of any particular disease. This strict distinction became a problem when trying to explain the disproportionate prevalence of AIDS in southern Africa and ultimately contributed to the tragedy of AIDS denialism in South Africa. In tandem with viewing how the perceived divide between multi-causal and mono-causal theories underpinned AIDS denialism, I examine Thabo Mbeki’s specific role, while acknowledging that AIDS is being deprioritised on a broader international level. Overall, I will demonstrate that any long-term plan to eliminate AIDS will require viral and socio-economic factors to be considered simultaneously and that such a theoretical approach requires a clearer understanding of the underlying concepts of disease aetiology. (shrink)

Defining disease and delineating its boundaries is a contested area in contemporary philosophy of medicine. The leading naturalistic theory faces a new round of difficulties related to defining a normal environment alongside normal organismic functioning and to delineating a discrete boundary between risk factors and disease. Normative theories face ongoing and seemingly intractable difficulties related to value pluralism and the problematic relation between theory and practice. In this article, I argue for an integral—as opposed to a hybrid—philosophy of health based (...) on Bernard Lonergan’s notion of generalized empirical method that provides a way to settle these difficulties dynamically and comprehensively, both in theory, by orienting functional and statistical investigation toward an explanatory ecological viewpoint, and in practice, by framing critiques in relation to the normativity intrinsic to all human inquiry. (shrink)

This paper considers an important recent contribution by Miguel Hernán to the ongoing debate about causal inference in epidemiology. Hernán rejects the idea that there is an in-principle epistemic distinction between the results of randomized controlled trials and observational studies: both produce associations which we may be more or less confident interpreting as causal. However, Hernán maintains that trials have a semantic advantage. Observational studies that seek to estimate causal effect risk issuing meaningless statements instead. The POA proposes a solution (...) to this problem: improved restrictions on the meaningful use of causal language, in particular “causal effect”. This paper argues that new restrictions in fact fail their own standards of meaningfulness. The paper portrays the desire for a restrictive definition of causal language as positivistic, and argues that contemporary epidemiology should be more realistic in its approach to causation. In a realist context, restrictions on meaningfulness based on precision of definition are neither helpful nor necessary. Hernán’s favoured approach to causal language is saved from meaninglessness, along with the approaches he rejects. (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)

Cancer—and scientific research on cancer—raises a variety of compelling philosophical questions. This entry will focus on four topics, which philosophers of science have begun to explore and debate. First, scientific classifications of cancer have as yet failed to yield a unified taxonomy. There is a diversity of classificatory schemes for cancer, and while some are hierarchical, others appear to be “cross-cutting,” or non-nested. This literature thus raises a variety of questions about the nature of the disease and disease classification. Second, (...) philosophers of science have historically taken the aim of science to be arriving at true theories. However, scientists studying cancer come from a variety of disciplines, with different scientific as well as practical aims. Perhaps it is not surprising, then, that historians and philosophers of science do not seem to agree on how best to characterize the aim and structure of cancer research; it is far from clear whether the appropriate characterization of the aim is arriving at true theories, or even whether the proper units of analysis are “theories”, or instead, “models”, “explanatory frameworks”, “research programs”, “paradigms”, or perhaps, “experimental traditions”. With the rise of “big data” science—such as the Cancer Genome Atlas Project (or TCGA)—and “systems” approaches to the study of disease, both philosophers and historians of science are rethinking how best to describe and explain these distinctive kinds of scientific inquiry. -/- Third, cancer is in part a byproduct of our developmental and life history, as well as our evolutionary history. Cancer progression can be compared to a reversion of development, or, to the evolution of multicellularity. Thus, cancer raises intriguing questions about how we conceive of “functions”, “development”, and the role of our evolutionary history and particularly, selective trade-offs, in vulnerability to disease. -/- Last but not least, cancer research provides a case study for consideration of the roles of values at the science-policy interface. Epidemiological and toxicological research on cancer’s causes informs toxics law and regulatory policy, which raises a variety of questions about the nature of evidence and inductive risk in such contexts. (shrink)