BackgroundResearch using data from medical care promises to advance medical science and improve healthcare. Academia is not the only sector that expects such research to be of great benefit. The research-based health industry is also interested in so-called ‘real-world’ health data to develop new drugs, medical technologies or data-based health applications. While access to medical data is handled very differently in different countries, and some empirical data suggest people are uncomfortable with the idea of companies accessing health information, this paper (...) aims to advance the ethical debate about secondary use of medical data generated in the public healthcare sector by for-profit companies for medical research (ReuseForPro).MethodsWe first clarify some basic concepts and our ethical-normative approach, then discuss and ethically evaluate potential claims and interests of relevant stakeholders: patients as data subjects in the public healthcare system, for-profit companies, the public, and physicians and their healthcare institutions. Finally, we address the tensions between legitimate claims of different stakeholders in order to suggest conditions that might ensure ethically sound ReuseForPro.ResultsWe conclude that there are good reasons to grant for-profit companies access to medical data if they meet certain conditions: among others they need to respect patients’ informational rights and their actions need to be compatible with the public’s interest in health benefit from ReuseForPro. (shrink)

It is widely acknowledged that trust plays an important role for the acceptability of data sharing practices in research and healthcare, and for the adoption of new health technologies such as AI. Yet there is reported distrust in this domain. Although in the UK, the NHS is one of the most trusted public institutions, public trust does not appear to accompany its data sharing practices for research and innovation, specifically with the private sector, that have been introduced in recent years. (...) In this paper, we examine the question of, what is it about sharing NHS data for research and innovation with for-profit companies that challenges public trust? To address this question, we draw from political theory to provide an account of public trust that helps better understand the relationship between the public and the NHS within a democratic context, as well as, the kind of obligations and expectations that govern this relationship. Then we examine whether the way in which the NHS is managing patient data and its collaboration with the private sector fit under this trust-based relationship. We argue that the datafication of healthcare and the broader ‘health and wealth’ agenda adopted by consecutive UK governments represent a major shift in the institutional character of the NHS, which brings into question the meaning of public good the NHS is expected to provide, challenging public trust. We conclude by suggesting that to address the problem of public trust, a theoretical and empirical examination of the benefits but also the costs associated with this shift needs to take place, as well as an open conversation at public level to determine what values should be promoted by a public institution like the NHS. (shrink)

BackgroundDespite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.MethodsSeventeen semi-structured interviews were conducted with birth parents (...) and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.ResultsThree themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.ConclusionsThe ethical values that underscore birth parents and mothers’ perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI. (shrink)

‘Trust but verify’ is a translation of a Russian proverb made famous by former US President Ronald Reagan. In their paper, Grahamet alappear to take an alternate view that might be summarised astrust or verify. The contrast highlights a general question: how do we come to trust in authorities? More specifically, Grahamet alclaim: (1) that UK Trusted Research Environments (TREs) are misnamed as future custodians for big health data because their promised verification systems actually negate the uncertainty that trust requires; (...) (2) the public is mistaken if it believes such verification enhances trust; (3) the notion of building public trust in TREs is unclear or misconceived. In response, I propose a more relational, perhaps less reductionist account. I argue (1) that verification is itself a source of uncertainty, so it can’t extinguish the uncertainty needed for trust; (2) it’s nevertheless possible for verification to enhance feelings of trust thereby reducing our needs for the same; (3) trust is also social, even political, meaning institutions like TREs may become too big to fail—and end up shielding their ‘trusted’ brand by being less candid about inevitable flaws in their verification systems. (shrink)

In our recent paper ‘Trust and the Goldacre Review: Why TREs are not about trust’1 we argue that trusted research environments (TREs) reduce the need for trust in the use and sharing of health data, and that referring to these data storage systems as ‘trusted’ raises a number of concerns. Recent replies to our paper have raised several objections to this argument. In this reply, we seek to build on the arguments presented in our original paper, address some of the (...) misunderstanding of our position expressed in these replies, and sketch out where further research is needed. One of the central arguments of our original paper was that the language we use to describe data-sharing initiatives matters, because the way things like TREs are presented and framed makes a difference to what people can, and should, expect from them. However, there is a more basic and obvious problem with referring to these institutions as ‘trusted’: it begs the question about whether these institutions are, in fact, trusted. We cannot know in advance of these initiatives being implemented, established, and appropriate assessments completed, whether they are actually trusted. Further questions abound: whom might TREs eventually be trusted by? If some people end up trusting them, but not others, is it appropriate to call them ‘trusted’? At best, this is simply a case of wishful thinking (and a more accurate name would be ‘hopefully-TREs’). More problematically, this may be an instance of ‘trustworthiness-signalling’: an attempt to convince others to trust, without meeting the requirements for genuine trustworthiness. By calling TREs ‘trusted’, the implication seems to be that placing our trust in them is perfectly fine: they are the kinds of things that are trusted by others and can safely be trusted by us. But not only does simply calling something ‘trusted’ …. (shrink)

BackgroundBiobanks and biomedical research data repositories collect their samples and associated data from volunteer participants. Their aims are to facilitate biomedical research and improve health, and they are framed in terms of contributing to the public good. Biobank resources may be accessible to researchers with commercial motivations, for example, researchers in pharmaceutical companies who may utilise the data to develop new clinical therapeutics and pharmaceutical drugs. Studies exploring citizen perceptions of public/private interactions associated with large health data repositories/biobanks indicate that (...) there are sensitivities around public/private and/or non-profit/profit relationships and international sample and data sharing. Less work has explored how biobanks communicate their public/private partnerships to the public or to their potential research participants. MethodsWe explored how a biobank’s aims, benefits and risks, and private/public relationships have been framed in public facing recruitment documents.ResultsBiobank documents often communicate their commercial access arrangements but not the detail about what these interactions would entail, and how risks and benefits would be distributed to the public.ConclusionWe argue that this leads to a polarised discourse between public and private entities and/or activities, and fails to attend to the blurred lines between them. This results in a lack of attention to more important issues such as how risks and benefits in general are distributed to the public. We call for a nuanced approach that can contribute to the much-needed dialogue in this space. (shrink)

BackgroundAs the use of AI becomes more pervasive, and computerised systems are used in clinical decision-making, the role of trust in, and the trustworthiness of, AI tools will need to be addressed. Using the case of computational phenotyping to support the diagnosis of rare disease in dysmorphology, this paper explores under what conditions we could place trust in medical AI tools, which employ machine learning.MethodsSemi-structured qualitative interviews with stakeholders who design and/or work with computational phenotyping systems. The method of constant (...) comparison was used to analyse the interview data.ResultsInterviewees emphasized the importance of establishing trust in the use of CP technology in identifying rare diseases. Trust was formulated in two interrelated ways in these data. First, interviewees talked about the importance of using CP tools within the context of a trust relationship; arguing that patients will need to trust clinicians who use AI tools and that clinicians will need to trust AI developers, if they are to adopt this technology. Second, they described a need to establish trust in the technology itself, or in the knowledge it provides—epistemic trust. Interviewees suggested CP tools used for the diagnosis of rare diseases might be perceived as more trustworthy if the user is able to vouchsafe for the technology’s reliability and accuracy and the person using/developing them is trusted.ConclusionThis study suggests we need to take deliberate and meticulous steps to design reliable or confidence-worthy AI systems for use in healthcare. In addition, we need to devise reliable or confidence-worthy processes that would give rise to reliable systems; these could take the form of RCTs and/or systems of accountability transparency and responsibility that would signify the epistemic trustworthiness of these tools. words 294. (shrink)