There is a well-developed literature on trust. Distrust, on the other hand, has gathered far less attention in the philosophical literature. A recent exception to that trend in the philosophical literature is Hawley who develops a unified account of both trust and distrust. My aim in this paper is to present arguments against her account of trust and distrust, though then to also suggest a patch.

When it comes to using patient data from the National Health Service for research, we are often told that it is a matter of trust: we need to trust, we need to build trust, we need to restore trust. Various policy papers and reports articulate and develop these ideas and make very important contributions to public dialogue on the trustworthiness of our research institutions. But these documents and policies are apparently constructed with little sustained reflection on the nature of trust (...) and trustworthiness, and therefore are missing important features that matter for how we manage concerns related to trust. We suggest that what we mean by ‘trust’ and ‘trustworthiness’ matters and should affect the policies and guidance that govern data sharing in the NHS. We offer a number of initial, general reflections on the way in which some of these features might affect our approach to principles, policies and strategies that are related to sharing patient data for research. This paper is the outcome of a ‘public ethics’ coproduction activity which involved members of the public and two academic ethicists. Our task was to consider collectively the accounts of trust developed by philosophers as they applied in the context of the NHS and to coproduce an argumentative position relevant to this context. (shrink)

Background Trust is an essential phenomenon of relationship between patients and healthcare professionals and can be described as an accepted vulnerability to the power of another person over something that one cares about in virtue of goodwill toward the trustor. This characterization of interpersonal trust appears to be adequate for patients suffering from chronic illness. Trust is especially important in the context of chronic cardiovascular diseases as one of the main global health problems. Research Aim The purpose of the qualitative (...) study was to gain a deeper understanding of how people with chronic cardiovascular disease experience and make sense of trust in healthcare professionals. Research Design Eleven semi-structured interviews with participants analysed using interpretative phenomenological analysis to explore in detail their lived experience of trust as a relational phenomenon. Participants and Research Context Participants with chronic cardiovascular disease were purposively recruited from inpatients on the cardiology ward of the university hospital located in central Slovakia. Ethical Considerations The study was approved by the faculty ethics committee. Participants gave their written informed consent. Findings Four Interrelated Group Experiential Themes Sense of co-existence; Belief in competence; Will to help; Ontological security with eight subthemes were identified. The findings describe the participants' experience with trust in healthcare professionals as a phenomenon of close co-existence, which is rooted in the participants’ vulnerability and dependence on the goodwill and competence of health professionals to help with the consequence of (re)establishing a sense of ontological security in the situation of chronic illness. Conclusion Findings will contribute to an in-depth understanding of trust as an existential dimension of human co-existence and an ethical requirement of healthcare practice, inspire patient empowerment interventions, support adherence to treatment, and person-centred care. (shrink)

Trust and trustworthiness are essential for good healthcare, especially in mental healthcare. New technologies, such as mobile health apps, can affect trust relationships. In mental health, some apps need the trust of their users for therapeutic efficacy and explicitly ask for it, for example, through an avatar. Suppose an artificial character in an app delivers healthcare. In that case, the following questions arise: Whom does the user direct their trust to? Whether and when can an avatar be considered trustworthy? Our (...) study aims to analyze different dimensions of trustworthiness in the context of mobile health app use. We integrate O'Neill's account of autonomy, trust, and trustworthiness into a model of trustworthiness as a relational concept with four relata: B is trustworthy with respect to A regarding the performance of Z because of C. Together with O'Neill's criteria of trustworthiness (honesty, competence, and reliability), this four-sided model is used to analyze different dimensions of trustworthiness in an exemplary case of mobile health app use. Our example focuses on an app that uses an avatar and is intended to treat sleep difficulties. The conceptual analysis shows that interpreting trust and trustworthiness in health app use is multi-layered and involves a net of interwoven universal obligations. At the same time, O'Neill's approach to autonomy, trust, and trustworthiness offers a normative account to structure and analyze these complex relations of trust and trustworthiness using mobile health apps. (shrink)

In recent years, increasingly advanced artificial intelligence (AI), and in particular machine learning, has shown great promise as a tool in various healthcare contexts. Yet as machine learning in medicine has become more useful and more widely adopted, concerns have arisen about the “black-box” nature of some of these AI models, or the inability to understand—and explain—the inner workings of the technology. Some critics argue that AI algorithms must be explainable to be responsibly used in the clinical encounter, while supporters (...) of AI dismiss the importance of explainability and instead highlight the many benefits the application of this technology could have for medicine. However, this dichotomy fails to consider the particular ways in which machine learning technologies mediate relations in the clinical encounter, and in doing so, makes explainability more of a problem than it actually is. We argue that postphenomenology is a highly useful theoretical lens through which to examine black-box AI, because it helps us better understand the particular mediating effects this type of technology brings to clinical encounters and moves beyond the explainability stalemate. Using a postphenomenological approach, we argue that explainability is more of a concern for physicians than it is for patients, and that a lack of explainability does not introduce a novel concern to the physician–patient encounter. Explainability is just one feature of technological mediation and need not be the central concern on which the use of black-box AI hinges. (shrink)

Two challenges have faced policymakers during the Covid-19 pandemic: First, they must determine the reliability of expert testimony in the face of uncertainty; second, they must determine the relevance of different kinds of expertise with regard to particular decisions. I argue that both these problems can be fruitfully analyzed through the lens of trust by introducing an in-depth case study of Iceland’s handling of the first wave of the Covid-19 pandemic. I contend that the problem of relevance highlights the limited (...) appeal of a thoroughly technocratic society. Value judgments, best realized through democratic processes, are always lurking in the background. (shrink)

BackgroundThe ability of healthcare to protect sensitive personal data in medical records and registers might influence public trust, which in turn might influence willingness to allow healthcare to use such data. The aim of this study was to examine how the general public’s trust relates to their attitudes towards uses of health data.MethodsA stratified sample from the general Swedish population received a questionnaire about their willingness to share health data. Respondents were also asked about their trust in the management and (...) protection of electronic health data.ResultsA large majority (81.9%) of respondents revealed high levels of trust in the ability of healthcare to protect electronic patient data. Good health was associated with significantly higher levels of trust compared to bad health. Respondents with low levels of trust were significantly less willing to allow personal data to be used for different purposes and were more inclined to insist on being asked for permission beforehand. Those with low levels of trust also perceived risks of unauthorized access to personal data to be higher and the likely damage of such unauthorized access worse, compared to those with high levels of trust.ConclusionsTrust in the ability of healthcare to protect electronic health is generally high in Sweden. Those with higher levels of trust are more willing to let their data be used, including without informed consent. It thus seems crucial to promote trust in order to be able to reap the benefits that digitalization makes possible through increased access and use of data in healthcare. (shrink)

The UK Government’s Code of Conduct for data-driven health and care technologies, specifically artificial intelligence -driven technologies, comprises 10 principles that outline a gold-standard of ethical conduct for AI developers and implementers within the National Health Service. Considering the importance of trust in medicine, in this essay I aim to evaluate the conceptualisation of trust within this piece of ethical governance. I examine the Code of Conduct, specifically Principle 7, and extract two positions: a principle of rationally justified trust that (...) posits trust should be made on sound epistemological bases and a principle of value-based trust that views trust in an all-things-considered manner. I argue rationally justified trust is largely infeasible in trusting AI due to AI’s complexity and inexplicability. Contrarily, I show how value-based trust is more feasible as it is intuitively used by individuals. Furthermore, it better complies with Principle 1. I therefore conclude this essay by suggesting the Code of Conduct to hold the principle of value-based trust more explicitly. (shrink)

The UK Government’s Code of Conduct for data-driven health and care technologies, specifically artificial intelligence -driven technologies, comprises 10 principles that outline a gold-standard of ethical conduct for AI developers and implementers within the National Health Service. Considering the importance of trust in medicine, in this essay I aim to evaluate the conceptualisation of trust within this piece of ethical governance. I examine the Code of Conduct, specifically Principle 7, and extract two positions: a principle of rationally justified trust that (...) posits trust should be made on sound epistemological bases and a principle of value-based trust that views trust in an all-things-considered manner. I argue rationally justified trust is largely infeasible in trusting AI due to AI’s complexity and inexplicability. Contrarily, I show how value-based trust is more feasible as it is intuitively used by individuals. Furthermore, it better complies with Principle 1. I therefore conclude this essay by suggesting the Code of Conduct to hold the principle of value-based trust more explicitly. (shrink)

Philosophical Psychology is dedicating this issue on trust and trustworthiness to Katherine Hawley (1971–2021) for two reasons. First, she was an expert in the area. Hawley was one of the most rele...

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical (...) diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare. (shrink)

Over the last few years, policies have been introduced in the UK that identify and treat patients as potential organ donors before death. Patients incapacitated due to catastrophic brain injury may now undergo intensive ante-mortem interventions to improve the chances of successfully transplanting their organs into third parties after death. The most significant ethical and legal problem with these policies is that they are not based on the individual’s specific wishes in the circumstances. Policy-makers appear reluctant to inform potential registrants (...) on the Organ Donor Register about ante-mortem donor optimisation procedures and to provide an opportunity to record specific wishes in advance. They are reliant on blind trust in the organ donation programme, which as I argue in this paper, presents significant risks for the achievement of its aim of securing an adequate supply of organs for transplantation. I argue that informed trust, based on accurate information about the future consequences of registra... (shrink)

When are we obligated to believe someone? To what extent are people authorities about their own experiences? What kind of harm might we enact when we doubt? Questions like these lie at the heart of many debates in social and feminist epistemology, and they’re the driving issue behind a key conceptual framework in these debates—gaslighting. But while the concept of gaslighting has provided fruitful insight, it's also proven somewhat difficult to adjudicate, and seems prone to over-application. In what follows, I (...) argue that Katherine Hawley's theory of trust can provide a useful alternative lens for looking at contested testimony. To do this, I focus on a particularly complex—but increasingly popular—application of gaslighting: the physician/patient relationship, and the idea of ‘medical gaslighting’. I argue that, even though patients can experience harm when they are disbelieved, there are nevertheless good reasons for physicians not to trust patients about at least some of their own narratives. (shrink)

According to some philosophers and social scientists, mainstream economic theories currently play an unprecedented role in shaping human societies. This phenomenon can be linked to the dissemination of methodological individualism, where common goods are interpreted as reducible to aggregates of individuals' well-being. Nonetheless, some emergent difficulties of economics in coping with global institutional issues have encouraged some authors to revise that paradigm. In the last three decades, there has been a parallel growing philosophical interest in investigating social sciences' epistemological and (...) ontological foundations. Unfortunately, these two research trends have often not communicated systematically enough to understand the specific ontology of collective aims. It is still unclear whether some irreducible social goods exist and how the answer to this question can set up a consistent understanding of cooperative actions. This dissertation addresses this issue, drawing on philosophical investigations about collective intentionality. It claims that a coherent ontological taxonomy of irreducible and reducible social goods is theoretically viable and can reframe prevailing approaches to collective action dilemmas influenced by the traditional economic categorisation of goods. The research begins by analysing how the influential theory of public goods has paved the way for some social and moral impasses. Furthermore, some theoretical proposals suggesting the existence of relational goods and common-pool resources are scrutinised. Hence, a refutation of the ontological reduction of all social goods to individual ones is developed, arguing that a specific form of cooperation, grounded in we-intentions and we-reasoning, shows the plausibility of the existence of some kinds of irreducible collective goods. Expanding on that view, a refinement of this categorisation of social goods is put forward by underlying the function of some relational entities, such as trust and shared sensemaking processes, in making cooperation robust in uncertain environments. Finally, by applying the taxonomy to collective action gridlocks regarding climate change, digital information, and public education, the consistency of this hypothesis is tested, showing that a pluralistic ontological account of social goods might improve the moral understanding of current social dilemmas. (shrink)