Should we be worried that the concept of trust is increasingly used when we assess non-human agents and artefacts, say robots and AI systems? Whilst some authors have developed explanations of the concept of trust with a view to accounting for trust in AI systems and other non-agents, others have rejected the idea that we should extend trust in this way. The article advances this debate by bringing insights from conceptual engineering to bear on this issue. After setting up a (...) target concept of trust in terms of four functional desiderata (trust-reliance distinction, explanatory strength, tracking affective responses, and accounting for distrust), I analyze how agential vs. non-agential accounts can satisfy these. A final section investigates how ‘non-ideal’ circumstances—that is, circumstances where the manifest and operative concept use diverge amongst concept users—affect our choice about which rendering of trust is to be preferred. I suggest that some prominent arguments against extending the language of trust to non-agents are not decisive and reflect on an important oversight in the current debate, namely a failure to address how narrower, agent-centred accounts curtail our ability to distrust non-agents. (shrink)

This paper proposes a novel way to understand trust in blockchain technology by analogy with trust placed in institutions. In support of the analysis, a detailed investigation of institutional trust is provided, which is then used as the basis for understanding the nature and ethical limits of blockchain trust. Two interrelated arguments are presented. First, given blockchains’ capacity for being institution-like entities by inviting expectations similar to those invited by traditional institutions, blockchain trust is argued to be best conceptualized as (...) a specialized form of trust in institutions. Keeping only the core functionality and certain normative ideas of institutions, this technology broadens our understanding of trust by removing the need for third parties while retaining the value of trust for the trustor. Second, the paper argues that blockchains’ decentralized nature and the implications and effects of this decentralization on trust issues are double-edged. With the erasure of central points, the systems simultaneously crowd out the pivotal role played by traditional institutions and a cadre of representatives in meeting their assigned obligations and securing the functional systems’ trustworthy performances. As such, blockchain is positioned as a technology containing both disruptive features that can be embedded with meaningful normative values and inherent ethical limits that pose a direct challenge to the actual trustworthiness of blockchain implementations. Such limits are proposed to be ameliorated by facilitating a shift of responsibility to the groups of people directly associated with the engendering of trust in the blockchain context. (shrink)

Artificial intelligence (AI) plays a rapidly increasing role in clinical care. Many of these systems, for instance, deep learning-based applications using multilayered Artificial Neural Nets, exhibit epistemic opacity in the sense that they preclude comprehensive human understanding. In consequence, voices from industry, policymakers, and research have suggested trust as an attitude for engaging with clinical AI systems. Yet, in the philosophical and ethical literature on medical AI, the notion of trust remains fiercely debated. Trust skeptics hold that talking about trust (...) in nonhuman agents constitutes a category error and worry about the concept being misused for ethics washing. Proponents of trust have responded to these worries from various angles, disentangling different concepts and aspects of trust in AI, potentially organized in layers or dimensions. Given the substantial disagreements across these accounts of trust and the important worries about ethics washing, we embrace a diverging strategy here. Instead of aiming for a positive definition of the elements and nature of trust in AI, we proceed ex negativo, that is we look at cases where trust or distrust are misplaced. Comparing these instances with trust expedited in doctor–patient relationships, we systematize these instances and propose a taxonomy of both misplaced trust and distrust. By inverting the perspective and focusing on negative examples, we develop an account that provides useful ethical constraints for decisions in clinical as well as regulatory contexts and that highlights how we should not engage with medical AI. (shrink)

Trust constitutes a fundamental strategy to deal with risks and uncertainty in complex societies. In line with the vast literature stressing the importance of trust in doctor–patient relationships, trust is therefore regularly suggested as a way of dealing with the risks of medical artificial intelligence (AI). Yet, this approach has come under charge from different angles. At least two lines of thought can be distinguished: (1) that trusting AI is conceptually confused, that is, that we cannot trust AI; and (2) (...) that it is also dangerous, that is, that we should not trust AI—particularly if the stakes are as high as they routinely are in medicine. In this paper, we aim to defend a notion of trust in the context of medical AI against both charges. To do so, we highlight the technically mediated intentions manifest in AI systems, rendering trust a conceptually plausible stance for dealing with them. Based on literature from human–robot interactions, psychology and sociology, we then propose a novel model to analyse notions of trust, distinguishing between three aspects: reliability, competence, and intentions. We discuss each aspect and make suggestions regarding how medical AI may become worthy of our trust. (shrink)

The exponential growth of algorithms has made establishing a trusted relationship between human and artificial intelligence increasingly important. Algorithm systems such as chatbots can play an important role in assessing a user’s credibility on algorithms. Unless users believe the chatbot’s information is credible, they are not likely to be willing to act on the recommendation. This study examines how literacy and user trust influence perceptions of chatbot information credibility. Results confirm that algorithmic literacy and users’ trust play a pivotal role (...) in how users form perceptions of the credibility of chatbot messages and recommendations. Insights on how user trust is related to credibility provide a useful perspective on the conceptualization of algorithmic credibility. Algorithmic information processing that has been identified provides better foundations for algorithm design and development and a stronger basis for the design of sense-making chatbot journalism. (shrink)

This paper sets out an account of trust in AI as a relationship between clinicians, AI applications, and AI practitioners in which AI is given discretionary authority over medical questions by clinicians. Compared to other accounts in recent literature, this account more adequately explains the normative commitments created by practitioners when inviting clinicians’ trust in AI. To avoid committing to an account of trust in AI applications themselves, I sketch a reductive view on which discretionary authority is exercised by AI (...) practitioners through the vehicle of an AI application. I conclude with four critical questions based on the discretionary account to determine if trust in particular AI applications is sound, and a brief discussion of the possibility that the main roles of the physician could be replaced by AI. (shrink)

Trust and monitoring are traditionally antithetical concepts. Describing trust as a property of a relationship of reliance, we introduce a theory of trust and monitoring, which uses mathematical models based on two classes of functions, including _q_-exponentials, and relates the levels of trust to the costs of monitoring. As opposed to several accounts of trust that attempt to identify the special ingredient of reliance and trust relationships, our theory characterizes trust as a quantitative property of certain relations of reliance that (...) can be quantified and expressed as a scalar quantity. Our theory is applicable to both human–human and human–artificial agent interactions, as it is agnostic with respect to the concrete realization of trustworthiness properties, and is compatible with many views differing on which properties contribute to trust and trustworthiness. Finally, as our mathematical models make the quantitative features of trust measurable, they provide empirical studies on trust with a rigorous methodology for its measurement. (shrink)

The use of artificial intelligence (AI) in hiring entails vast ethical challenges. As such, using an ethical lens to study this phenomenon is to better understand whether and how AI matters in hiring. In this paper, we examine whether ethical perceptions of using AI in the hiring process influence individuals’ trust in the organizations that use it. Building on the organizational trust model and the unified theory of acceptance and use of technology, we explore whether ethical perceptions are shaped by (...) individual differences in performance expectancy and social influence and how they, in turn, impact organizational trust. We collected primary data from over 300 individuals who were either active job seekers or who had recent hiring experience to capture perceptions across the full range of hiring methods. Our findings indicate that performance expectancy, but not social influence, impacts the ethical perceptions of AI in hiring, which in turn influence organizational trust. Additional analyses indicate that these findings vary depending on the type of hiring methods AI is used for, as well as on whether participants are job seekers or individuals with hiring experience. Our study offers theoretical and practical implications for ethics in HRM and informs policy implementation about when and how to use AI in hiring methods, especially as it pertains to acting ethically and trustworthily. (shrink)

In his recent article ‘Limits of trust in medical AI,’ Hatherley argues that, if we believe that the motivations that are usually recognised as relevant for interpersonal trust have to be applied to interactions between humans and medical artificial intelligence, then these systems do not appear to be the appropriate objects of trust. In this response, we argue that it is possible to discuss trust in medical artificial intelligence, if one refrains from simply assuming that trust describes human–human interactions. To (...) do so, we consider an account of trust that distinguishes trust from reliance in a way that is compatible with trusting non-human agents. In this account, to trust a medical AI is to rely on it with little monitoring and control of the elements that make it trustworthy. This attitude does not imply specific properties in the AI system that in fact only humans can have. This account of trust is applicable, in particular, to all cases where a physician relies on the medical AI predictions to support his or her decision making. (shrink)

In this paper, I will identify two problems of trust in an AI-relevant context: a theoretical problem and a practical one. I will identify and address a number of skeptical challenges to an AI-relevant theory of trust. In addition, I will identify what I shall term the ‘scope challenge’, which I take to hold for any AI-relevant theory of trust that purports to be representationally adequate to the multifarious forms of trust and AI. Thereafter, I will suggest how trust-engineering, a (...) position that is intermediate between the modified pure rational-choice account and an account that gives rise to trustworthy AI, might allow us to address the practical problem of trust, before identifying and critically evaluating two candidate trust-engineering approaches. (shrink)

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around (...) cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts. (shrink)

What ethical principles should autonomous machines follow? How do we implement these principles, and how do we evaluate these implementations? These are some of the critical questions Vivek Nallur asks in his essay “Landscape of Machine Implemented Ethics (2020).” He provides a broad, insightful survey of answers to these questions, especially focused on the implementation question. In this commentary, I will first critically summarize the main themes and conclusions of Nallur’s essay and then expand upon the landscape that Nallur presents (...) by suggesting additional approaches to machine ethics. The approaches I discuss reflect normative ethical theories and need not be applied only to machines, although machine ethics is the focus here. The overall goal is to open up further questions and research possibilities as society searches for the best approach to machine ethics. (Due to an unfortunate miscommunication with the copy editor an important reference was omitted from this article. The reference that should be included is: Nallur, V. (2020). Landscape of Machine Implemented Ethics. Science and Engineering Ethics 26 (5):2381-2399.). (shrink)

In this paper I argue that Artificial Intelligence and the many data science methods associated with it, such as machine learning and large language models, are first and foremost epistemic technologies. In order to establish this claim, I first argue that epistemic technologies can be conceptually and practically distinguished from other technologies in virtue of what they are designed for, what they do and how they do it. I then proceed to show that unlike other kinds of technology (_including_ other (...) epistemic technologies) AI can be uniquely positioned as an epistemic technology in that it is primarily designed, developed and deployed to be used in _epistemic contexts_ such as inquiry, it is specifically designed, developed and deployed to manipulate _epistemic content_ such as data, _and_ it is designed, developed and deployed to do so particularly through _epistemic operations_ such as prediction and analysis. As has been shown in recent work in the philosophy and ethics of AI (Alvarado, AI and Ethics, 2022a), understanding AI as an epistemic technology will also have significant implications for important debates regarding our relationship to AI technologies. This paper includes a brief overview of such implications, particularly those pertaining to explainability, opacity, trust and even epistemic harms related to AI technologies. (shrink)

With an increasing use of artificial intelligence (AI) systems, theorists have analyzed and argued for the promotion of trust in AI and trustworthy AI. Critics have objected that AI does not have the characteristics to be an appropriate subject for trust. However, this argumentation is open to counterarguments. Firstly, rejecting trust in AI denies the trust attitudes that some people experience. Secondly, we can trust other non‐human entities, such as animals and institutions, so why can we not trust AI systems? (...) Finally, human–AI trust is criticized based on a conception of human–human trust, which does not recognize the distinctiveness of the human–AI relationship. This article aims to refute these counterarguments based on the genealogical analyses of ‘trust’ and ‘trustworthiness’ of Karen Jones and Thomas Simpson, who show that trust and trustworthiness help to overcome vulnerabilities. This function of trust gives reason to use human–human trust as a standard. For this function, it is important that trustees are responsive to trust. While animals and institutions could be responsive, narrow AI systems are unable to be responsive to trust. Therefore, we should not apply trust to AI and instead direct our trust to those who can be responsive to and held responsible for our trust. (shrink)