Luke Golemon

A full answer to the problem of how to treat human brain organoids (HBOs) and other liminally conscious beings requires (at least) book-length analysis, but a significant part of the problem is disagreement not just about which moral principles apply to such beings, but which beings count as “conscious” at all. Van Gyseghem, Kierickx, and Barnhart (2025) survey the scientific, ethical, and philosophical literature surrounding HBOs and find an utter lack of consensus regarding which theory of consciousness is best, or even adequate; consequently, many different theories of consciousness feature in the surveyed papers. They propose we move beyond consciousness as the subject of ethical analysis; I agree. I propose a framework for first-line ethical analysis with the more tractable subject of sentience. I provide four brief arguments for this framework: parsimony, ecumenism, conservativism, and the treatment of permissive theories of consciousness such as Integrated Information Theory.

Recent work on conversational artificial intelligence (CAI) in mental healthcare highlights many interesting and controversial features. Sedlakova, Lucivero, Pavarini, and Kerasidou (2025) focus on one particular feature: epistemic trust. To avoid problems endemic to the unsettled literature, Sedlakova et al propose that we treat CAI as fictional characters (11-13). They draw on the noted epistemic and normative powers of imagination, fiction, and simulation in order to ground epistemic trust or something sufficiently normatively similar. I leave holistic evaluation of their proposal to other authors. Instead, I note that Sedlakova et al use a nonstandard analysis of epistemic grounding, drawn from Fabian Dorsch's account of perceptual (rather than more general epistemic) grounding in fictions, opening them to easily avoidable objections. I reconstruct their position with the dominant view, avoiding these objections and revealing an alternative conclusion regarding the differences between CAI and human therapists in mental health care: the question is not whether CAI can provide grounded knowledge, but whether CAI can provide fully grounded care.

This paper defends a new argument in the spirit of Golemon and Graber’s Deductive No-Miracles Argument (DNMA) for scientific realism, recently criticized by Kok Yong Lee. While Lee argues that the DNMA is invalid or leads to a collapse in probability assignments, I develop an Inductive No-Miracles Argument (INMA) that preserves logical strength without relying on abductive inference or explanatory premises. By introducing an “approximately equal” probabilistic operator (≈), I show how probabilistic reasoning can license a strong inductive inference without collapsing probability calculus. The resulting INMA satisfies the desiderata motivating the DNMA and clarifies how probabilistic reasoning can underwrite realist inference without appeal to explanatory virtue. Moreover, the structure argued for seems ripe for use in other contexts where avoiding abduction in lieu of less theory-laden probability is attractive, such as fine-tuning arguments and evolutionary debunking arguments.

A recent series of papers have challenged whether black box AI challenges informed consent. Director has most recently argued that the explicit canonical view of informed consent and the implicit view held by bioethicists neglects a crucial distinction between first-order and higher-order evidence needed to show that black box AI does not threaten efficacious consent. I believe a charitable reconstruction of the canonical view and the implicit view held by bioethicists does not neglect the crucial distinction and, indeed, many clear cases of informed consent involve a notable lack of first-order information but a wealth of second-order information. Thus, it most reasonable to interpret the ‘informed’ portion of ‘informed consent’ as (i) relative to what we know, and (ii) inclusive of both first- and higher-order information. This strengthens the argument for Director’s conclusions about black box AI: its purported threat to informed consent are instead worries about the reliability of black box AI.

The traditional No-Miracles Argument (TNMA) asserts that the novel predictive success of science would be a miracle, and thus too implausible to believe, if successful theories were not at least approximately true. The TNMA has come under fire in multiple ways, challenging each of its premises and its general argumentative structure. While the TNMA relies on explaining novel predictive success via the truth of the theories, we put forth a deductive version of the No-Miracles argument (DNMA) that avoids inference to the best explanation entirely. Instead, a relatively simple empirical framework and a probabilistic analysis can accomplish the ambitious goals of the TNMA while entirely sidestepping its problems. This close-but-distinct argument has many independent strengths and comparatively few weaknesses. Indeed, objections tailored specifically to the DNMA reveal surprising insights into how exactly NMAs are neither circular nor question-begging, as has been widely speculated.

Automated vehicles promise much in the way of both economic boons and increased personal safety. For better or worse, the effects of automating personal vehicles will not be felt for some time. In contrast, the effects of automated work vehicles, like semi-trucks, will be felt much sooner—within the next decade. The costs and benefits of automation will not be distributed evenly; while most of us will be positively affected by the lower prices overall, those losing their livelihoods to the automated semi-trucks and other similar work vehicles will be much worse off. This sets up a classic distributive justice problem: how do we balance the harms and benefits of automation? In this paper, the authors recommend work alternatives: policies that the government would enact in order to ensure that each person could live a decent life even if one could not work, especially for reasons like having one’s job automated. After arguing for this position, we propose some policy guidelines.

In their recent article, Graber and Golemon (_Sophia_ 1–18, 2019 ) argue that any attempted evolutionary debunking of naturalism faces a dilemma. First, in order to be evolutionarily plausible, the skeptical implications must not be too broad. Second, in order to constitute a genuine challenge to scientific realism, the skeptical implications must not be too narrow. Graber and Golemon further develop an evolutionary debunking argument that avoids both horns of this dilemma. De Ray (_Erkenntnis_ 1–21, 2020 ) criticizes Graber and Golemon’s debunking argument then develops his own, competing debunking argument. We first respond to de Ray’s criticisms by showing how Graber and Golemon’s argument can be extended so as to avoid de Ray’s critique. We then show that de Ray’s argument debunking argument is impaled by both horns of Graber and Golemon’s dilemma. The ultimate result will be both a better understanding of the desiderata and the argumentative contours of a successful evolutionary debunking argument.

With by far the lowest population density in the United States, myriad challenges attach to healthcare delivery in Alaska. In the “Size, Population, and Accessibility” section, we characterize this geographic context, including how it is exacerbated by lack of infrastructure. In the “Distributing Healthcare” section, we turn to healthcare economics and staffing, showing how these bear on delivery—and are exacerbated by geography. In the “Health Care in Rural Alaska” section, we turn to rural care, exploring in more depth what healthcare delivery looks like outside of Alaska’s major cities. This discussion continues in the “Alaska’s Native Villages” section, which specifically analyzes healthcare in Alaska’s indigenous villages, some of the smallest and most isolated communities in the United States. Though many of the ways we could improve Alaskan health care for Alaskan residents are limited by its unique features, the “Justice and Healthcare Delivery” and “Technology and Telemedicine” sections consider ways in which certain policies and technology—including telemedicine—could mitigate the challenges developed in previous sections.

Reprinted with modification and permission from Kennedy Institute of Ethics Journal. The phenomenon of medical overtesting in general, and specifically in the emergency room, is well-known and regarded as harmful to both the patient and the healthcare system. Although the implications of this problem raise myriad ethical concerns, this chapter explores the extent to which overtesting might mitigate race-based health inequalities. Given that medical malpractice and error greatly increase when the patients belong to a racial minority, it is no surprise that the mortality rate similarly increases in proportion to white patients. For these populations, an environment that emphasizes medical overtesting may well be the desirable medical environment until care evens out among races and ethnicities; additionally, efforts to lower overtesting in conjunction with a high rate of racist medical mythology may cause harm by lower testing when it is actually warranted. Furthermore, medical overtesting may help to assuage racial distrust. This paper ultimately concludes that an environment of medical overtesting may be less pernicious than the alternative.

The phenomenon of medical overtesting in general, and specifically in the emergency room, is well-known and regarded as harmful to both the patient and the healthcare system. Although the implications of this problem raise myriad ethical concerns, this paper explores the extent to which overtesting might mitigate race-based health inequalities. Given that medical malpractice and error greatly increase when the patients belong to a racial minority, it is no surprise that the mortality rate similarly increases in proportion to white patients. For these populations, an environment that emphasizes medical overtesting may well be the desirable medical environment until care evens out among races and ethnicities; additionally, efforts to lower overtesting in conjunction with a high rate of racist medical mythology may cause harm by lower testing when it is actually warranted. Furthermore, medical overtesting may help to assuage racial distrust. This paper ultimately concludes that an environment of medical overtesting may be less pernicious than the alternative.

While Plantinga has famously argued that acceptance of neo-Darwinian theory commits one to the rejection of naturalism, Plantinga’s argument is vulnerable to an objection developed by Evan Fales. Not only does Fales’ objection undermine Plantinga’s original argument, it establishes a general challenge which any attempt to revitalize Plantinga’s argument must overcome. After briefly laying out the contours of this challenge, we attempt to meet it by arguing that because a purely naturalistic account of our etiology cannot explain the correlation between our preference for simplicity and simplicity’s ability to serve as a veridical method of theory selection, the scientific realist is committed to the rejection of naturalism.