Participation in research is supposed to be voluntary and informed. Yet it is difficult to ensure people are adequately informed about the potential uses of their biological materials when they donate samples for future research. We propose a novel consent framework which we call “demonstrated consent” that leverages blockchain technology and generative AI to address this problem. In a demonstrated consent model, each donated sample is associated with a unique non-fungible token (NFT) on a blockchain, which records in its metadata (...) information about the planned and past uses of the sample in research, and is updated with each use of the sample. This information is accessible to a large language model (LLM) customized to present this information in an understandable and interactive manner. Thus, our model uses blockchain and generative AI technologies to track, make available, and explain information regarding planned and past uses of donated samples. (shrink)

Since their public launch, a little over a year ago, large language models (LLMs) have inspired a flurry of analysis about what their implications might be for medical ethics, and for society more broadly. 1 Much of the recent debate has moved beyond categorical evaluations of the permissibility or impermissibility of LLM use in different general contexts (eg, at work or school), to more fine-grained discussions of the criteria that should govern their appropriate use in specific domains or towards certain (...) ends. 2 With each passing week, it seems more and more inevitable that LLMs will be a pervasive feature of many, if not most, of our lives. It would not be possible—and would not be desirable—to prohibit them across the board. We need to learn how to live with LLMs; to identify and mitigate the risks they pose to us, to our fellow creatures, and the... (shrink)

When making substituted judgments for incapacitated patients, surrogates often struggle to guess what the patient would want if they had capacity. Surrogates may also agonize over having the (sole) responsibility of making such a determination. To address such concerns, a Patient Preference Predictor (PPP) has been proposed that would use an algorithm to infer the treatment preferences of individual patients from population-level data about the known preferences of people with similar demographic characteristics. However, critics have suggested that even if such (...) a PPP were more accurate, on average, than human surrogates in identifying patient preferences, the proposed algorithm would nevertheless fail to respect the patient’s (former) autonomy since it draws on the ‘wrong’ kind of data: namely, data that are not specific to the individual patient and which therefore may not reflect their actual values, or their reasons for having the preferences they do. Taking such criticisms on board, we here propose a new approach: the Personalized Patient Preference Predictor (P4). The P4 is based on recent advances in machine learning, which allow technologies including large language models to be more cheaply and efficiently ‘fine-tuned’ on person-specific data. The P4, unlike the PPP, would be able to infer an individual patient’s preferences from material (e.g., prior treatment decisions) that is in fact specific to them. Thus, we argue, in addition to being potentially more accurate at the individual level than the previously proposed PPP, the predictions of a P4 would also more directly reflect each patient’s own reasons and values. In this article, we review recent discoveries in artificial intelligence research that suggest a P4 is technically feasible, and argue that, if it is developed and appropriately deployed, it should assuage some of the main autonomy-based concerns of critics of the original PPP. We then consider various objections to our proposal and offer some tentative replies. (shrink)

Artificial intelligence (AI), large language models (LLMs), such as Open AI’s ChatGPT, have a remarkable ability to process and generate human language but have also raised complex and novel ethica...

In their Target Article, Rahimzadeh et al. (2023) discuss the virtues and vices of employing ChatGPT in ethics education for healthcare professionals. To this end, they confront the chatbot with a moral dilemma and analyse its response. In interpreting the case, ChatGPT relies on Beauchamp and Childress’ four prima-facie principles: beneficence, non-maleficence, respect for patient autonomy, and justice. While the chatbot’s output appears admirable at first sight, it is worth taking a closer look: ChatGPT not only misses the point when (...) applying the principle of non-maleficence; its response also fails, in several places, to honour patient autonomy – a flaw that should be taken seriously if large language models are to be employed in ethics education. I therefore subject ChatGPT’s reply to detailed scrutiny and point out where it went astray. (shrink)

Recent developments in AI and robotics enable people to create _personalised digital duplicates_ – these are artificial, at least partial, recreations or simulations of real people. The advent of such duplicates enables people to overcome their individual scarcity. But this comes at a cost. There is a common view among ethicists and value theorists suggesting that individual scarcity contributes to or heightens the value of a life or parts of a life. In this paper, we address this topic. We make (...) five main points. First, that there is a plausible _prima facie_ case for the scarcity threat: AI may undermine the value of an individual human life by making us less scarce. Second, notwithstanding this prima facie threat, the role of scarcity in individual value is disputable and always exists in tension with the contrasting view that scarcity is a tragedy that limits our value. Third, there are two distinct forms of scarcity – instrumental and intrinsic – and they contribute to value in different ways. Fourth, digital duplication technology may undermine instrumental scarcity, to at least some extent, but the axiological consequences of this are highly variable. Fifth, digital duplication technology does not affect intrinsic scarcity, and may actually heighten it. (shrink)

This paper argues that one problem that besets black-box AI is that it lacks algorithmic justifiability. We argue that the norm of shared decision making in medical care presupposes that treatment decisions ought to be justifiable to the patient. Medical decisions are justifiable to the patient only if they are compatible with the patient’s values and preferences and the patient is able to see that this is so. Patient-directed justifiability is threatened by black-box AIs because the lack of rationale provided (...) for the decision makes it difficult for patients to ascertain whether there is adequate fit between the decision and the patient’s values. This paper argues that achieving algorithmic transparency does not help patients bridge the gap between their medical decisions and values. We introduce a hypothetical model we call Justifiable AI to illustrate this argument. Justifiable AI aims at modelling normative and evaluative considerations in an explicit way so as to provide a stepping stone for patient and physician to jointly decide on a course of treatment. If our argument succeeds, we should prefer these justifiable models over alternatives if the former are available and aim to develop said models if not. (shrink)

Scientific research is supposed to acquire or generate knowledge, but such a purpose would be severely undermined by instances of research misconduct (RM) and questionable research practices (QRP). RM and QRP are often framed in terms of moral transgressions by individuals (bad apples) whose aberrant acts could be made conducive by shortcomings in regulatory measures of organizations or institutions (bad barrels). This notion presupposes, to an extent, that the erring parties know exactly what they are doing is wrong and morally (...) culpable, but had nonetheless proceeded to commit wrongful acts. However, a confession of intent to deceived is often not readily admitted by perpetrators of RM. I posit that beyond the simplistic notion of conscious moral transgression, deficits in epistemic virtues and/or the prevalence of epistemic vices have important roles to play in initiating and driving RM/QRP. For the individual perpetrator, deficits in epistemic virtues could lead to or amplify errors in one’s desperate attempt to be accomplished or to excel, and pushes one across the ethical line or down the slippery slope of misconduct. Likewise, a lack of epistemic virtue within perpetrators’ institution or organization could make it conducive for deceitful acts and suppress indications and warning signs for the former. Furthermore, epistemic vices exhibited by reviewers, editors and journals could also promote RM/QRP. In this view, epistemic failings, rather than widespread moral deficiencies of individuals within the research ecosystem, may underlie the prevalence of RM/QRP. (shrink)

OpenAI’s Chat Generative Pre-training Transformer (ChatGPT), Google’s Bard and other generative artificial intelligence (GenAI) technologies can greatly enhance the capability of healthcare profess...

Cohen (2023), Rahimzadeh and colleagues (2023), and Porsdam Mann and colleagues (2023) have written thorough and well-canvassed pieces about the ethical and conceptual challenges of large language...

Contrary to how it may seem when we observe its output, an [LLM] is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to...

Porsdam Mann and colleagues provide an overview of opportunities and risks associated with the use of personalized large language models (LLMs) for text production in bio)ethics (Porsdam Mann et al...

Salloch and Eriksen (2024) present a compelling case for including patients as co-reasoners in medical decision-making involving artificial intelligence (AI). Drawing on O'Neill’s neo-Kantian frame...

In this reply to our commentators, we respond to ethical concerns raised about the potential use (or misuse) of personalized LLMs for academic idea and prose generation, including questions about c...

Danaher and Nyholm ( 2024a ) have recently proposed that digital duplicates—such as fine-tuned, “personalized” large language models that closely mimic a particular individual—might reduce that individual’s _scarcity_ and thus increase the amount of instrumental value they can bring to the world. In this commentary, we introduce the notion of _relational scarcity_ and explore how digital duplicates would affect the value of interpersonal relationships.

The potential of using personalized Large Language Models (LLMs) or “generative AI” (GenAI) to enhance productivity in academic research, as highlighted by Porsdam Mann and colleagues (Porsdam Mann...

Large Language Models (LLMs) “assign probabilities to sequences of text. When given some initial text, they use these probabilities to generate new text. Large language models are language models u...

In their recent paper “AUTOGEN: A Personalized Large Language Model for Academic Enhancement—Ethics and Proof of Principle,” Porsdam Mann et al. (2023) demonstrate a technique for fine-tuning the l...

Generative artificial intelligence (GenAI) promises to revolutionize data-driven fields (Milmo 2023). Building on decades of large language modeling (LLM) (Toner 2023), GenAI can collect, harmonize...

Cohen’s article (2023) on the significance of ChatGPT for bioethics suggests that little is known about the development of generative AI (“GAI”) in China and other national markets. It warns about...

In the target article, Porsdam Mann and colleagues (2023) broadly survey the ethical opportunities and risks of using general and personalized large language models (LLMs) to generate academic pros...

As Cohen (2023) describes, the discourse around ChatGPT has been focused on potential risks while AI-based chatbots could also positively empower patients. There are other potential benefits to Cha...