Complex technology has become an essential aspect of everyday life. We rely on technology as part of basic infrastructure and repeatedly for tasks throughout the day. Yet, in many cases the relation surpasses mere reliance and evolves to trust in technology. A new, disruptive technology is blockchain. It claims to introduce trustless relationships among its users, aiming to eliminate the need for trust altogether—even being described as “the trust machine”. This paper presents a proposal to adjust the concept of trust (...) in blockchain applications with the tools provided by conceptual engineering. Its goal is to propose a concept of trust, that offers more than a halo term, devoid of the normative depth that the original, philosophical term actually carries. To evaluate possible concepts of trust in blockchain applications, five criteria are proposed: These criteria ensure that the conceptual relation indeed embodies trust, thereby being richer than mere reliance, and that the chosen concept highlights the active role of the trustor. While the concepts of trust in engineers and trust in algorithms have to be discarded, institutional trust emerges as a viable candidate, given some refinements. Ultimately, the concept of trust by normative expectations and attribution is suggested to capture the essence of trust in blockchain applications. (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...

This paper develops a novel account of the nature of disinformation that challenges several widely spread theoretical assumptions, such as that disinformation is a species of information, a species of misinformation, essentially false or misleading, essentially intended/aimed/having the function of generating false beliefs in/misleading hearers. The paper defends a view of disinformation as ignorance generating content: on this account, X is disinformation in a context C iff X is a content unit communicated at C that has a disposition to generate (...) ignorance at C in normal conditions. I also offer a taxonomy of disinformation, and a view of what it is for a signal to constitute disinformation for a particular agent in a particular context. The account, if correct, carries high stakes upshots, both theoretically and practically: disinformation tracking will need to go well beyond mere fact checking. (shrink)

Where does entitlement to trust come from? When we trust someone to φ, do we need to have reason to trust them to φ or do we start out entitled to trust them to φ by default? Reductivists think that entitlement to trust always “reduces to” or is explained by the reasons that agents have to trust others. In contrast, anti-reductivists think that, in a broad range of circumstances, we just have entitlement to trust. even if we don’t have positive (...) reasons to do so. In this paper, we argue for a version of anti-reductivism. Roughly, we argue that we have default entitlement to trust someone to φ so long as there is an operative norm that requires S to φ. At least in such circumstances (and absent defeaters), we don’t need any positive reasons to trust S to φ. (shrink)

Simion and Kelp explain the trustworthiness of an AI based on that AI’s disposition to meet its obligations. Roughly speaking, according to Simion and Kelp, an AI is trustworthy regarding its task if and only if that AI is obliged to complete the task and its disposition to complete the task is strong enough. Furthermore, an AI is obliged to complete a task in the case where the task is the AI’s etiological function or design function. This account has a (...) strength in that it can provide a unificatory rationale for the trustworthy-making properties of AIs. According to this account, being explainable, being safe, and being transparent are the trustworthy-making properties of an AI because an AI can fulfill its etiological or design functions in the case where it is explainable, safe, and transparent. This paper shows that though Simion and Kelp’s account has a strength, this account is not satisfactory for two reasons. The first reason is that an AI’s trustworthiness is not determined just by the AI’s disposition to meet obligations, and the second reason is that it is difficult to explain how an AI’s etiological function and design function have to do with that AI’s obligations. To provide a full-fledged account, Simion and Kelp should dismiss these concerns. (shrink)

In the current philosophical literature on trustworthiness, two claims are very widely accepted, first that trustworthiness is a kind of reliability and secondly, that trustworthiness is not a virtue. Both claims are made, for instance, in Hawley's recent highly influential account of trustworthiness. I argue that both are mistaken. I develop and defend a new account of trustworthiness as responsibility, contrasting it with reliability and obligation accounts of trustworthiness. I argue that trustworthiness as responsibility is very plausibly a kind of (...) moral virtue. (shrink)

A theme running through Katherine Hawley’s recent works on trust and trustworthiness is that thinking about the relations between these and Miranda Fricker’s notion of testimonial injustice offers a perspective from which we can see several limitations of Fricker’s own account of testimonial injustice. This paper clarifies the aspects of Fricker’s account that Hawley’s criticisms target, focusing on her objections to Fricker’s proposal that its primary harm involves a kind of epistemic objectification and her characterization of testimonial injustice in terms (...) of prejudicial credibility deficits. I also offer an assessment of the potency of Hawley’s objections, concluding that they do point to genuine limitations of Fricker’s account, but that we can appreciate Hawley’s points without adopting her trust-centric approach to testimonial injustice, or her specific commitment-based accounts of trust and trustworthiness. However, in the last section I examine the positive picture of testimonial injustice that emerges from Hawley’s discussion of unequally distributed obstacles to both being trustworthy and maintaining a reputation for trustworthy testimony in her recent book How To Be Trustworthy, considering how this picture contrasts with Fricker’s account and where it might have important affinities with other accounts in the literature. (shrink)

A widespread assumption in debates about trust and trustworthiness is that the evaluative norms of principal interest on the trustor’s side of a cooperative exchange regulate trusting attitudes and performances whereas those on the trustee’s side regulate dispositions to respond to trust. The aim here will be to highlight some unnoticed problems with this asymmetrical picture – and in particular, how it elides certain key evaluative norms on both the trustor’s and trustee’s side the satisfaction of which are critical to (...) successful cooperative exchanges – and to show that a symmetrical, ‘achievement-first’ approach to theorising about trust and trustworthiness (and their relation to each other) has important advantages by comparison. The view I develop is guided by a structural analogy with practical reasoning. Just as practical reasoning is working as it should only when there is realisation (knowledge and action) of states (belief and intention) with reverse directions of fits (mind-to-world and world-to-mind), likewise, cooperation between trustor and trustee is functioning as it should only when there is an analogous kind of realisation on both sides of the cooperative exchange – viz., when the trustor ‘matches’ her achievement in trusting (an achievement in fitting reliance to reciprocity) with the trustee’s achievement in responding to trust (an achievement in fitting reciprocity to reliance). An upshot of viewing cooperation between trustor and trustee as exhibiting achievement-theoretic structure is that we will be better positioned to subsume trustworthiness (and its cognates on the trustee’s side), like trust, under a wider suite of evaluative norms that regulate attempts, dispositions, and achievements symmetrically on both sides of a cooperative exchange, with ‘matching achievements’ as the gold standard. (shrink)

The aim of this paper is to better qualify the problem of online trust. The problem of online trust is that of evaluating whether online environments have the proper design to enable trust. This paper tries to better qualify this problem by showing that there is no unique answer, but only conditional considerations that depend on the conception of trust assumed and the features that are included in the environments themselves. In fact, the major issue concerning traditional debates surrounding online (...) trust is that those debates focus on specific definitions of trust and specific online environments. Ordinarily, a definition of trust is assumed and then environmental conditions necessary for trust are evaluated with respect to such specific definition. However, this _modus operandi_ fails to appreciate that trust is a rich concept, with a multitude of meanings and that there is still no strict consensus on which meaning shall be taken as the proper one. Moreover, the fact that online environments are constantly evolving and that new design features might be implemented in them is completely ignored. In this paper, the richness of the philosophical discussions about trust is brought into the analysis of online trust. I first provide a set of conditions that depend on the definition of trust that can be assumed and then discuss those conditions with respect to the design of online environments in order to determine whether they can enable (and under which circumstances) trust. (shrink)

Simion and Kelp develop the obligation-based account of trustworthiness as a compelling general account of trustworthiness and then apply this account to various instances of AI. By doing so, they explain in what way any AI can be considered trustworthy, as per the general account. Simion and Kelp identify that any account of trustworthiness that relies on assumptions of agency that are too anthropocentric, such as that being trustworthy, must involve goodwill. I argue that goodwill is a necessary condition for (...) being trustworthy and further suggest a network account of trustworthy AI, which retains the goodwill as the essential requirement for the concept of trustworthiness and meanwhile predicts that current AI can be trustworthy. The alternative account suggests that the focus of trustworthy AI is not merely AI technology, but the whole network of AI involving AI technology, AI designers, AI companies, and other social and legal institutions. A trustworthy AI requires that AI technology is reliable and that other involved agents are trustworthy. (shrink)

The doxastic view (DV) of trust maintains that trust essentially involves belief. In a recent paper, Arnon Keren (Citation2020) gestures toward a new objection to the view, labeled Trust’s Meno Problem (TMP), which calls into question the DV’s ability to explain the widely held intuition that trust has distinct and indispensable value. As of yet, there has been no attempt to take up TMP on behalf of DV. This paper aims to fill precisely this lacuna. I do so in three (...) main stages. In §1 I contextualize and elucidate the problem, to which Keren gestures but does not address in detail. In §2 I disambiguate multiple possible interpretations of TMP, seeking to identify the most philosophically challenging. Finally, in §3, I argue that DV can solve even this interpretation. In order to do so, I make use ofthe highly plausible claim we find in the work of Katherine Hawley (Citation2012, 2019): that trust pays a compliment to the trustee. The payoffs of exploring the doxastic view in the context of Trust’s Meno Problem are twofold: we better understand the nature of the problem itself, and we see that the doxastic view can give a satisfying answer. (shrink)

AbstractSimion and Kelp offer a prima facie very promising account of trustworthy AI. One benefit of the account is that it elegantly explains trustworthiness in the case of cancer diagnostic AIs, which involve the acquisition by the AI of a representational etiological function. In this brief note, I offer some reasons to think that their account cannot be extended — at least not straightforwardly — beyond such cases (i.e., to cases of AIs with non-representational etiological functions) without incurring the unwanted (...) cost of overpredicting untrustworthiness. (shrink)