Brian D . Earp
Brian D. Earp, PhD is an Associate Professor of Biomedical Ethics within the Yong Loo Lin School of Medicine at the National University of Singapore (NUS) and, by courtesy, Associate Professor of Philosophy and of Psychology. Also a Research Associate of the Uehiro Oxford Institute of the University of Oxford, Brian directs the Oxford-NUS Centre for Neuroethics and Society, and is Associate Director of the Yale-Hastings Program in Ethics and Health Policy at Yale University and The Hastings Center. In 2022, Brian was elected to the UK Young Academy under the auspices of the British Academy and the Royal Society.
Brian’s work is cross-disciplinary, following training in philosophy, cognitive science, experimental psychology, history and sociology of science and medicine, and ethics. A co-recipient of the 2018 Daniel M. Wegner Theoretical Innovation Prize from the Society for Personality and Social Psychology, Brian was also one of four named finalists for the 2020 John Maddox Prize for “standing up for science” (awarded by Nature). Brian is also recipient of both the Robert G. Crowder Prize in Psychology and the Ledyard Cogswell Award for Citizenship from Yale University, where, as an undergraduate, Brian was elected President of the Yale Philosophy Society and served as Editor-in-Chief of the Yale Philosophy Review.
Brian then conducted graduate research in psychological methods as a Henry Fellow of New College at the University of Oxford, followed by a degree in the history, philosophy, and sociology of science, technology, and medicine as a Cambridge Trust Scholar and Rausing Award recipient at Trinity College at the University of Cambridge. After spending a year in residence as the inaugural Presidential Scholar in Bioethics at The Hastings Center in Garrison, New York, Brian was appointed Benjamin Franklin Resident Graduate Fellow while completing a dual Ph.D. in philosophy and psychology at Yale University. Brian’s essays have been translated into Polish, German, Italian, Spanish, French, Portuguese, Japanese, and Hebrew.
Brian’s work is cross-disciplinary, following training in philosophy, cognitive science, experimental psychology, history and sociology of science and medicine, and ethics. A co-recipient of the 2018 Daniel M. Wegner Theoretical Innovation Prize from the Society for Personality and Social Psychology, Brian was also one of four named finalists for the 2020 John Maddox Prize for “standing up for science” (awarded by Nature). Brian is also recipient of both the Robert G. Crowder Prize in Psychology and the Ledyard Cogswell Award for Citizenship from Yale University, where, as an undergraduate, Brian was elected President of the Yale Philosophy Society and served as Editor-in-Chief of the Yale Philosophy Review.
Brian then conducted graduate research in psychological methods as a Henry Fellow of New College at the University of Oxford, followed by a degree in the history, philosophy, and sociology of science, technology, and medicine as a Cambridge Trust Scholar and Rausing Award recipient at Trinity College at the University of Cambridge. After spending a year in residence as the inaugural Presidential Scholar in Bioethics at The Hastings Center in Garrison, New York, Brian was appointed Benjamin Franklin Resident Graduate Fellow while completing a dual Ph.D. in philosophy and psychology at Yale University. Brian’s essays have been translated into Polish, German, Italian, Spanish, French, Portuguese, Japanese, and Hebrew.
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Artificial Intelligence by Brian D . Earp
detecting patterns clinicians would miss, quantifying previously invisible policy trade-offs,
and generating fine-grained predictions. Yet better evidence does not resolve moral
uncertainty; it can deepen it. When decision-makers gain access to information that was
formerly unknowable, they face a greater number of morally significant choices and must
justify the decisions that follow. This special e-issue of the Journal of Medical Ethics brings
together nine articles unified by this central insight. The first set of articles explores how AI
expands what we can know—and thereby what we are responsible for—across resource
allocation, disorders of consciousness, patient preference prediction, and clinician–patient
communication. The second set examines how embedding AI in clinical workflows
complicates the attribution of moral responsibility, addressing dual-use risks in
pharmaceutical development, health equity in clinical documentation, local fairness, causal
responsibility in human–AI systems, and meaningful human control. Together, the
contributions show that as AI broadens the scope of foreseeable outcomes, robust and
context-sensitive frameworks for distributing responsibility become necesssary.
life models (DLMs), or personalized AI systems trained on individual values and decision patterns, might help navigate this dilemma. DLMs offer a novel approach for anticipatory ethical reflection, potentially enabling individuals to simulate their responses to genetic information before irreversible disclosure.
The above quote from philosopher Will MacAskill captures the key tenets of “longtermism,” an ethical standpoint that places the onus on current generations to prevent AI- related—and other—X-Risks for the sake of people living in the future. Developing from an adjacent social movement commonly associated with utilitarian philosophy, “effective altruism,” longtermism has amassed following of its own. Its supporters argue that preventing X-Risks is at least as morally significant as addressing current challenges like global poverty. However, critics are concerned that such a distant-future focus will sideline efforts to tackle the many pressing moral issues facing humanity now. Indeed, according to “strong” longtermism, future needs arguably should take precedence over present ones. In essence, the claim is that there is greater expected utility to allocating available resources to prevent human extinction in the future than there is to focusing on present lives, since doing so stands to benefit the incalculably large number of people in later generations who will far outweigh existing populations. Taken to the extreme, this view suggests it would be morally permissible, or even required, to actively neglect, harm, or destroy large swathes of humanity as it exists today if this would benefit or enable the existence of a sufficiently large number of future—that is, hypothetical or potential—people, a conclusion that strikes many critics as dangerous and absurd.
Large language models (LLMs) may offer a partial solution to these challenges. These generative AI systems can provide interactive, age-appropriate explanations of medical procedures, risks, and outcomes tailored to each child’s comprehension level. LLMs could be designed to adapt their responses to young patients’ cognitive and emotional needs while supporting parents with clear, accessible medical information.
This paper examines the ethical implications of using LLMs in paediatric consent, focusing on balancing autonomy promotion with protecting children’s best interests. We explore how LLMs could be used to empower children to express preferences, mediate family disputes, and facilitate informed consent. However, important concerns arise: Can LLMs adequately support developing autonomy? Might they exert undue influence or worsen conflicts between family members and healthcare providers?
We conclude that while LLMs could enhance paediatric consent processes with appropriate safeguards and careful integration into clinical practice, their implementation must be approached cautiously. These systems should complement rather than replace the essential human elements of empathy, judgment, and trust in paediatric consent.
Given the complex nature of consent in clinical research, which involves both written documentation (in the form of participant information sheets and informed consent forms) and in-person conversations with a researcher, the use of LLMs raises significant concerns about the adequacy of existing regulatory frameworks. Institutional Review Boards (IRBs) will need to consider substantial reforms to accommodate the integration of LLM-based consent processes. We explore five potential models for LLM implementation, ranging from supplementary roles to complete replacements of current consent processes, and offer recommendations for researchers and IRBs to navigate the ethical landscape. Thus, we aim to provide practical recommendations to facilitate the ethical introduction of LLM-based consent in research settings by considering factors such as participant understanding, information accuracy, human oversight and types of LLM applications in clinical research consent.
framed as being conducted by either Consent-GPT, a junior doctor, or the treating surgeon. Participants broadly agreed that AI-supported consent was valid (87.6% agreement), but rated it significantly lower than consent sought solely by human
clinicians (treating surgeon: 97.6% agreement; junior doctor: 96.2%). Participants expressed substantially lower satisfaction with AI-supported consent compared to human-only processes (Consent-GPT: 59.5% satisfied; treating surgeon 96.8%; junior
doctor: 93.1%), despite identical consent interactions (i.e., the same informational content and display format). Regarding justification to sue the hospital following a complication, participants were slightly more inclined to support legal action in response to AI-supported consent than human-only consent. However, the strongest predictor was proper risk disclosure, not the consent-seeking agent. As AI integration in healthcare accelerates, these results highlight critical considerations for implementation strategies, suggesting that a hybrid approach to consent delegation that leverages AI’s information sharing capabilities while preserving meaningful human engagement may be more acceptable to patients than an otherwise identical process with relatively less human-to-human interaction.