Daan Kenis

This paper proposes using the term “Bioethics-in-Science” (BiS) to denote and delineate a set of emerging methodological approaches entangling bioethical inquiry directly within scientific practice. Drawing inspiration from Van Rensselaer Potter’s original vision of bioethics as a collaborative bridge between biology and ethics, BiS represents a community of practice aiming to contribute to bioethical inquiry in three key ways. First, it reframes the relationship between ethics and science by treating scientific practice as inherently value-laden and recognizing scientists’ relevant ethical expertise. Second, it develops methods—including ethics notebooks, collaborative workshops, and conceptual mapping—which facilitate a shift from mere external evaluation towards continuous ethical deliberation within research environments. Third, it demonstrates how bioethical inquiry can actively contribute to improving scientific practice through the co-creation of knowledge and values. Through case studies from synthetic biology, neurodiversity research, and proteomics, we illustrate how BiS arose from and operates in practice, and examine the challenges of crossing disciplinary boundaries. As such, we position BiS as offering a pathway for bioethical inquiry to move beyond its peripheral position vis-à-vis the sciences and engage meaningfully with the normative dimensions of scientific knowledge production instead, ultimately contributing to both better ethics and science.

Data-intensive healthcare has an equity problem. As data-intensive healthcare expands, historical injustices embedded in existing datasets limit its benefits for marginalized groups. While national and international Precision Medicine Research (PMR) initiatives - like All of Us (US) and the 100k Genome Project (UK) - aim to address these gaps through targeted inclusion, such strategies carry exploitative potential. Representative data serves not only underserved communities but also public and commercial interests, raising additional ethical concerns about how data gaps are filled. This paper critically examines targeted inclusion efforts and discussions thereof in PMR ethics, which tend to overlook the risks and social conditions participants face during research participation. Drawing on the concept of 'clinical labor', we introduce the idea of health data labor - the often invisible, unequal labor involved in health data generation. This framing helps expose how the burdens and risks of PMR participation may follow existing social inequities. We argue that without addressing these inequities in the participation process, inclusionary efforts risk reinforcing injustice. We conclude by proposing 'labor-sensitive research design' - a set of practices to make PMR more equitable in both process and outcome.

As healthcare systems prepare to implement AI-driven clinical decision support systems (CDSS), philosophical debate has increasingly examined the algorithmic normativity shaping the clinical encounter. Early discussions often framed AI as displacing physicians, while more recent work, inspired by medical hermeneutics, presents CDSS as dialogical ‘partners’, expanding interpretive resources within deliberative models of care. While a deliberative model presents a more accurate representation of the exact nature of the clinical encounter, opting for a dialogical model for AI implementation without attention to power risks obscuring how structural inequalities shape which voices are heard and which knowledges are valued. I argue that while a hermeneutical, dialogical framing of the physician–patient–AI triad presents a step forward, it fails to account for the algorithmic and biomedical normativity pervading each axis of this encounter. Drawing on feminist receptions of hermeneutics and the epistemic injustice in healthcare literature, I develop a repoliticized account of clinical dialogue attentive to these dynamics. Through a case study of endometriosis, I illustrate how CDSS may either entrench restrictive biomedical classifications or, if designed from the margins, expand hermeneutical resources and actively ameliorate care inequities. I conclude by sketching the contours of a feminist medical hermeneutics for AI-informed care, proposing “ameliorative medical AI” as a framework for leveraging CDSS as tools for advancing healthcare equity.

Precision medicine (PM) encompasses various emerging, data-intensive healthcare and biomedical research initiatives aimed at tailoring care to individual patient characteristics. While “precision” primarily denotes an epistemological shift in how biomedical research is approached and care delivered, the conviction that PM empowers patients in clinical decision-making is central to its vision as it is taken up across policy contexts. In this paper, I critically assess these promises by drawing on recent engagements in agential epistemic injustice (Lackey 2020; Medina 2022). I suggest that the social, cultural, and epistemological conditions in which the epistemic practice of precision care unfolds are conducive to epistemic injustice. Despite PM’s explicit aims to address longstanding criticisms regarding the disease-centric nature of contemporary biomedical care practices by including person-centered, non-biomedical features in clinical consideration, exploring its underlying logic suggests its epistemic economy is stacked against patients’ epistemic interests. As such, despite its laudable aim of patient empowerment, the exacerbated risk of epistemic injustice might truncate patients’ (epistemic) agency, further disempowering them in clinical decision-making. To conclude, I suggest that the reliance on “empowerment”- and “person-centered care”-rhetoric dominating PM discourse is a case of epistemic appropriation (Davis 2018), further discouraging engagement with social, experiential, and phenomenological dimensions of illness, defanging critics of raising those concerns, and impeding the realization of epistemic justice in healthcare.

This paper introduces Bioethics-in-Science (BiS), a novel methodological approach entangling bioethical inquiry directly within scientific practice. Drawing inspiration from Van Rensselaer Potter's original vision of bioethics as a collaborative bridge between biology and ethics, BiS represents a community of practice aimed to contribute to bioethical inquiry in three key ways. First, it reframes the relationship between ethics and science by treating scientific practice as inherently value-laden and recognizing scientists’ relevant ethical expertise. Second, it develops methods–including ethics notebooks, collaborative workshops, and conceptual mapping–that facilitate a shift from mere external evaluation towards continuous ethical deliberation within research environments. Third, it demonstrates how bioethical inquiry can actively contribute to improving scientific practice through the co-creation of knowledge and values. Through case studies from synthetic biology, neurodiversity research, and proteomics, we illustrate how BiS arose from and operates in practice and examine the challenges of crossing disciplinary boundaries. As such, we position BiS as offering a pathway for bioethical inquiry to move beyond its peripheral position vis-à-vis the sciences and engage meaningfully with the normative dimensions of scientific knowledge production instead, ultimately contributing to both better ethics and science.

In this chapter, we describe three areas within the broad field of ecogenomics or postgenomics: epigenetics, proteomics, and microbiomics. We argue that these fields challenge traditional bioethics in different ways. Since epigenetic, proteomic, and microbiomic data may contain phenotypical information, they may intensify discussions about consent, privacy, and return of results. But these fields also firmly position organisms, including human beings, as deeply entangled with their environments, as constituted by context, history, and experiences as much as genes. This yields new insights into concepts of health and development. We discuss precision medicine as an example of a systemic approach to health. We argue that acknowledging the entanglement of organism and environment also means recognizing the importance of interdisciplinarity for bioethics and thinking together biomedical ethics and environmental ethics.

In this chapter, we describe three areas within the broad field of ecogenomics or postgenomics: epigenetics, proteomics, and microbiomics. We argue that these fields challenge traditional bioethics in different ways. Since epigenetic, proteomic, and microbiomic data may contain phenotypical information, they may intensify discussions about consent, privacy, and return of results. But these fields also firmly position organisms, including human beings, as deeply entangled with their environments, as constituted by context, history, and experiences as much as genes. This yields new insights into concepts of health and development. We discuss precision medicine as an example of a systemic approach to health. We argue that acknowledging the entanglement of organism and environment also means recognizing the importance of interdisciplinarity for bioethics and thinking together biomedical ethics and environmental ethics.