Jonathan Adams

• Adams, Jonathan (2026). “Hermeneutic burden” and clinical responsibility: a response to Sparrow et al. on explanation and machine learning. Monash Bioethics Review. ISSN 1321-2753. doi: 10.1007/s40592-026-00300-6. Full text in Research Archive Show summary

  This paper critically reviews Sparrow et al.’s notion of the “hermeneutic burden” placed upon clinicians by the demand for explainable artificial intelligence (XAI) in the context of adaptive machine learning (ML) systems. While Sparrow et al. highlight important additional labour that may be required of clinicians, this response argues that framing explanation primarily in terms of such a burden obscures its overall ethical significance. This paper therefore offers a supplementary account of the interpretive work associated with XAI in medicine that places it within existing models of the patient–clinician relationship. In particular, Emanuel and Emanuel’s influential typology consisting of four models of the patient–physician relationship is used to extract possible justifications for the responsibility to grasp and explain not only patients’ values and conditions but also ML outputs. This allows us to distinguish between ‘hermeneutic burden’ and ‘hermeneutic responsibility’ and emphasise that explanation in medicine is not an incidental task but part of a clinician’s professional role, particularly on ‘interpretive’ and ‘deliberative’ models. The paper thus argues that viewing explanation as a hermeneutic responsibility linked to patient autonomy clarifies the ethical significance of XAI in terms of both the grounds and scope of clinicians’ responsibilities. At its core, the ethical challenge raised by XAI in clinical practice concerns not only the burdens it may impose on clinicians but also the evolution of clinicians’ traditional interpretive duties in the novel context of ML-mediated care.

• Adams, Jonathan (2026). Aesthetic injustice in healthcare: exploring testimonial and hermeneutical forms. Medicine, Health care and Philosophy. ISSN 1386-7423. doi: 10.1007/s11019-026-10334-6. Full text in Research Archive Show summary

  This paper introduces and develops the concepts of aesthetic testimonial injustice and aesthetic hermeneutical injustice, arguing that understanding the aesthetic dimensions of human experience, specifically those concerning physical appearance, is central to justice in healthcare practice. Drawing on Miranda Fricker’s framework of epistemic injustice, the paper provides a bridge between emerging literature on aesthetic injustice, which is often focused on lookism and appearance-based discrimination, and accounts that emphasize the recognition of individuals as aesthetic subjects. The concept of aesthetic testimonial injustice relates to the unjust discrediting of a person’s aesthetic testimony regarding their own body, often due to biases related to attractiveness, age or disability. Aesthetic hermeneutical injustice, by contrast, pertains to the structural disadvantage faced by individuals who lack the interpretive resources to make sense of or communicate their aesthetic experience as it pertains to bodily appearance, particularly in healthcare contexts. Using examples from prosthetics design, nursing care and disorders of visual perception, the paper shows how healthcare systems can marginalize patients’ aesthetic values and perceptions, thereby undermining their dignity and agency. It concludes by calling for a deeper understanding of aesthetics in bioethics, suggesting that respecting patients as embodied aesthetic agents can enhance ethical healthcare delivery and counter systemic injustice. The concepts of aesthetic testimonial injustice and aesthetic hermeneutical injustice thus expand bioethical discourse by highlighting the moral significance of aesthetic experience in healthcare contexts.

• Adams, Jonathan (2025). Artificial Intelligence and Understanding in Medicine. Bioethics. ISSN 0269-9702. doi: 10.1111/bioe.70049. Full text in Research Archive Show summary

  The paper explores the impact of artificial intelligence (AI) in medicine through a three‐dimensional model of medical understanding, which encompasses understanding of disease (via the biomedical perspective), illness (through first‐person experience) and sickness (which is seen as a social role). By linking these dimensions to the epistemic aims of transparency, certainty and empathy, the analysis highlights how AI's potential to enhance and undermine medical understanding can be debated. While AI may increase the transparency of bodily processes, improve diagnostic certainty and challenge dominant paradigms of clinical empathy, it also threatens to obscure value‐laden aspects of illness, erode confidence in subjective and social experiences of sickness and complicate mutual understanding in clinical encounters. Rather than offering an exhaustive empirical analysis of AI applications, this paper provides a conceptual framework for assessing AI's epistemic impacts in medicine, thereby serving as a foundation for future studies to examine specific implementations of AI.

• Adams, Jonathan (2025). Ethical and epistemic implications of artificial intelligence in medicine: a stakeholder-based assessment. AI & Society: Knowledge, Culture and Communication. ISSN 0951-5666. 40, p. 5935–5950. doi: 10.1007/s00146-025-02398-4. Full text in Research Archive Show summary

  As artificial intelligence (AI) technologies become increasingly embedded in high-stakes fields such as healthcare, ethical and epistemic considerations raise the need for evaluative frameworks to assess their societal impacts across multiple dimensions. This paper uses the ethical-epistemic matrix (EEM), a structured framework that integrates both ethical and epistemic principles, to evaluate medical AI applications more comprehensively. Building on the ethical principles of well-being, autonomy, justice, and explicability, the matrix introduces epistemic principles—accuracy, consistency, relevance, and instrumental efficacy—that assess AI’s role in knowledge production. This dual approach enables a nuanced assessment that reflects the diverse perspectives of stakeholders within the medical field—patients, clinicians, developers, the public, and health policy-makers—who assess AI systems differently based on distinct interests and epistemic goals. Although the EEM has been outlined conceptually before, no published research paper has yet used it explore the ethical and epistemic implications arising in its key intended application domain of AI in medicine. Through a systematic demonstration of the EEM as applied to medical AI, this paper argues that it encourages a broader understanding of AI’s implications and serves as a valuable methodological tool for evaluating future uses. This is illustrated with the case study of AI systems in sleep apnea detection, where the EEM highlights the ethical trade-offs and epistemic challenges that different stakeholders may perceive, which can be made more concrete if the tool is embedded in future technical projects.

• Adams, Jonathan (2024). Introducing the ethical-epistemic matrix: a principle-based tool for evaluating artificial intelligence in medicine. AI and Ethics. ISSN 2730-5953. doi: 10.1007/s43681-024-00597-1. Full text in Research Archive Show summary

  While there has been much discussion of the ethical assessment of artificial intelligence (AI) in medicine, such work has rarely been combined with the parallel body of scholarship analyzing epistemic implications of AI. This paper proposes a method for joint evaluation of AI’s ethical and epistemic implications in medicine that draws on the principle-oriented tradition in bioethics and the consequent ‘ethical matrix’ approach to assessing novel technologies. It first introduces principle-based approaches as specific tools for ethical assessment of AI in medicine and other domains that are contrasted with the lack of comparable epistemic principles that would govern AI evaluation in medicine. In the next section, the ethical matrix is explained as a well-established principle-based tool in applied ethics that has had some limited applications to near-term implications of AI in medicine and elsewhere that can be strengthened, I suggest, using epistemic principles. To this end, the following section looks to the philosophy of science for relevant epistemic principles, identifying ‘accuracy’, ‘consistency’, ‘relevance’, and ‘instrumental efficacy’ as a provisional set for technology evaluation. The next section articulates the relevance of these epistemic principles to AI in medicine by highlighting conventional standards that have already been applied in AI, epistemology, and the medical sciences. Before concluding, the paper then defines and defends the possibility of an ‘ethical-epistemic matrix’ for the application of these epistemic principles alongside established ethical principles to a selection of stakeholder groups: patients, clinicians, developers, and the public.

• Lopez, Marta Quemada; Plagemann, Thomas Peter; Goebel, Vera Hermine; Adams, Jonathan; Hui, David & Øverland, Britt [Show all 8 contributors for this article] (2024). Requirements Analysis for Responsible Explainable AI for Pediatric Sleep Apnea Diagnosis. In Pesch, Dirk (Eds.), Proceeding IEEE International Conference on Smart Computing 2024. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350349948. p. 362–367. doi: 10.1109/SMARTCOMP61445.2024.00082. Full text in Research Archive Show summary

  This paper presents a novel approach to requirements engineering for responsible explainable AI (rexAI) that pays attention to the broad range of explanation aspects and enforces the consideration of computer science, ethical, legal, and medical requirements. The medical use-case we are investigating is pediatric sleep apnea diagnosis. We analyze the non-functional and functional requirements and explore the multiple relationships and dependencies between the rexAI requirements and principles.

• Adams, Jonathan (2024). Examining Ethical and Social Implications of Digital Mental Health Technologies Through Expert Interviews and Sociotechnical Systems Theory. Digital Society: Ethics, Socio-Legal and Governance of Digital Technology. ISSN 2731-4650. doi: 10.1007/s44206-024-00110-5. Full text in Research Archive Show summary

  This paper aims to understand how science and technology experts working in the digital mental health field interpret the ethical and social implications of its technologies, combining an ‘expert interview’ methodology with insights from sociotechnical systems theory. Following recruitment of experts in science and technology fields who had experience of supporting the development of DMH interventions, 11 semi-structured interviews were conducted and analyzed in accordance with the Framework Method. A single theme of ‘complexity of implications’ is presented here and divided into the categories of ‘implications for users’, ‘implications for healthcare professionals and systems’, and ‘implications for society’. Participants identified a range of ethical and social implications of digital mental health technologies at the three different levels, which this discussion relates to three key aspects of complex sociotechnical systems identified in existing theoretical work. These are ‘heterogeneity’, ‘interdependence’ and ‘distribution’, each of which raises important questions for future research about how complex values, relationships and responsibilities should be negotiated in digital mental health. The paper concludes that this study’s approach provides a model for understanding the implications of digital health more broadly, with participants’ combined experience and knowledge shedding light on key interventions at the forefront of digitalization in healthcare.

• Dawkins, Shereen Cox Née; Kadlubsky, Alina; Svarverud, Ellen; Adams, Jonathan; Baraas, Rigmor C. & Bernabe, Rosemarie de La Cruz (2024). A scoping review of the ethics frameworks describing issues related to the use of extended reality. Open Research Europe. 4(74). doi: 10.12688/openreseurope.17283.2. Full text in Research Archive Show summary

  The use of extended reality (XR) / immersive technologies such as virtual, augmented, mixed reality and virtual worlds (Metaverse) raises issues of ethical concern. The various issues, if left unaddressed, may impact human wellbeing over time. Immersive technologies are used in entertainment, commerce, training, education, health, and the military among others. Subsequently, there is a broad spectrum of users with various degrees of competencies and vulnerabilities. Special attention regarding long-term effects of immersive technologies on children and the lack of consideration of inclusivity for all persons in society is essential. Several publications have highlighted ethical issues related to immersive technologies, and some have sought to address these issues by proposing solutions or approaches in the form of frameworks, codes of conduct or best practices. This review examined literature between 2000 and 2023 to identify proposed or adopted ethical frameworks, codes of conduct or best practices for immersive technologies. Qualitative research method was applied, using a scoping review approach. Twenty-eight papers were selected for analysis. Approximately 70% of the selected papers were published between 2020 and 2022. Using an inductive thematic analysis method, seven fundamental values and twenty-two corresponding principles were generated. The main values are respect for persons, well-being, safety, integrity and trust, justice, and responsiveness. The dominant principles identified are privacy, informed consent, responsibility, transparency, and freedom. The authors of the papers were predominantly academic researchers. The normative approaches to addressing ethical issues were organised into four domains: society and governance, industry, research/academic organisations, and individuals. Recommendations are: 1) development and/or application of laws or guidelines to ethical, legal, and social issues with immersive technologies; 2) adoption of inclusive approaches to design and development; 3) minimisation of risk for research participants; 4) empowerment of users of immersive technologies; and 5) promotion of responsibility and sincerity in the use of virtual space, especially in matters concerning identity and conduct.

• Adams, Jonathan (2023). Defending explicability as a principle for the ethics of artificial intelligence in medicine. Medicine, Health care and Philosophy. ISSN 1386-7423. doi: 10.1007/s11019-023-10175-7. Full text in Research Archive Show summary

  The difficulty of explaining the outputs of artificial intelligence (AI) models and what has led to them is a notorious ethical problem wherever these technologies are applied, including in the medical domain, and one that has no obvious solution. This paper examines the proposal, made by Luciano Floridi and colleagues, to include a new ‘principle of explicability’ alongside the traditional four principles of bioethics that make up the theory of ‘principlism’. It specifically responds to a recent set of criticisms that challenge the supposed need for such a principle to perform an enabling role in relation to the traditional four principles and therefore suggest that these four are sufficient without the addition of explicability. The paper challenges the critics’ premise that explicability cannot be an ethical principle like the classic four because it is explicitly subordinate to them. It argues instead that principlism in its original formulation locates the justification for ethical principles in a midlevel position such that they mediate between the most general moral norms and the contextual requirements of medicine. This conception of an ethical principle then provides a mold for an approach to explicability on which it functions as an enabling principle that unifies technical/epistemic demands on AI and the requirements of high-level ethical theories. The paper finishes by anticipating an objection that decision-making by clinicians and AI fall equally, but implausibly, under the principle of explicability’s scope, which it rejects on the grounds that human decisions, unlike AI’s, can be explained by their social environments.

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• Adams, Jonathan (2026). AI & Ethics. Full text in Research Archive
• Adams, Jonathan (2025). Whose Knowledge Counts? Exploring Epistemic Injustice in Medical AI (panel discussion). doi: https:/www.vaia.be/en/courses/whose-knowledge-counts. Full text in Research Archive
• Adams, Jonathan (2025). Structured Methods and Principles for the Ethics of AI in Healthcare. doi: https:/www.uio.no/dscience/english/news-and-events/events/25-ls-apr10.html. Full text in Research Archive
• Adams, Jonathan (2025). Can AI principlism without explicability be coherent? A response to Segers and De Proost. Theoretical Medicine and Bioethics. ISSN 1386-7415. 46(3), p. 268–268. doi: 10.1007/s11017-025-09709-2. Full text in Research Archive
• Dawkins, Shereen Cox Née; Svarverud, Ellen; Adams, Jonathan; Kadlubsky, Alina & Bernabe, Rosemarie de La Cruz (2024). A scoping review of the ethics frameworks describing issues related to the use of extended reality. doi: https:/zenodo.org/records/10854489. Full text in Research Archive
• Adams, Jonathan (2024). Epistemic Injustice and AI-Enabled mHealth. Full text in Research Archive
• Adams, Jonathan (2024). Ethical considerations of the use of artificial intelligence in research. Full text in Research Archive
• Adams, Jonathan (2024). De-mystifying AI in healthcare and medicine. Full text in Research Archive
• Lopez, Marta Quemada; Plagemann, Thomas Peter; Goebel, Vera Hermine; Adams, Jonathan; Hui, David & Øverland, Britt [Show all 8 contributors for this article] (2024). Requirements Analysis for Responsible Explainable AI for Pediatric Sleep Apnea Diagnosis. Full text in Research Archive
• Adams, Jonathan (2023). Explanation and transparency: bioethical resources for the future of AI in medicine. Full text in Research Archive
• Adams, Jonathan (2023). Review of 'Cross-Cultural and Religious Critiques of Informed Consent' edited by Joseph Tham, Alberto García Gómez and Mirko Daniel Garasic. Reading Religion. ISSN 2475-207X. Full text in Research Archive
• Adams, Jonathan & Hofmann, Bjørn Morten (2026). Responsible Explainable Artificial Intelligence in Medicine: Towards a Framework for Ethical and Epistemic Assessment. Universitetet i Oslo. ISSN 9788234808975. Full text in Research Archive Show summary

  This thesis is about responsible explainable artificial intelligence (AI) in medicine, aiming to build a principle-based framework to assess the knowledge that AI offers in the medical field and the ethical implications of AI in medicine. A key challenge that motivates this work is AI explainability: do we understand why AI systems produce the results that they do, and if not, then is that a problem for medical ethics? The thesis argues that medical ethics should adopt the emerging principle of ‘explicability’ to enable us to make ethical problems visible in AI systems and therefore connect traditional ethical principles to concrete responsibilities. The thesis argues that the principle of explicability, rather than being an unnecessary addition, resembles more established principles in its key focus on connecting abstract theory to clinical practice. The thesis also proposes a structured tool for evaluating medical AI, called ‘the ethical-epistemic matrix’. While ethical principles are often applied to medical AI, this matrix includes ‘epistemic’ (i.e. knowledge-related) principles, which assess the kinds of knowledge that AI systems can provide. The epistemic principles put forward are: accuracy, consistency, relevance and instrumental efficacy. This thesis also shows how the matrix can be used to explore the implications of medical AI by applying its principles to medical AI systems in general and for the specific case of sleep apnoea detection, which is relevant to the Respire project of which this PhD work is part. Overall, this thesis advances both the conceptual and methodological foundations for evaluating responsible explainable AI in medicine and provides a practical framework within which to conduct such evaluation. These contributions can support further development of principle- and matrix-based approaches to the evaluation of medical AI, as well as interdisciplinary collaborations aiming to implement responsible explainable medical AI in practice.

• Dawkins, Shereen Cox Née; Svarverud, Ellen; Adams, Jonathan; Kadlubsky, Alina; Bernabe, Rosemarie de La Cruz & Baraas, Rigmor C. (2023). Mapping of the ethical Issues in XR-overview of Ethical Frameworks: A Scoping Review. Zenodo. doi: https:/zenodo.org/records/10886403. Full text in Research Archive

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