The widely accepted view states that an intention to deceive is not necessary for lying. Proponents of this view, the so-called non-deceptionists, argue that lies are simply insincere assertions. We conducted three experimental studies with false explanations, the results of which put some pressure on non-deceptionist analyses. We present cases of explanations that one knows are false and compare them with analogical explanations that differ only in having a deceptive intention. The results show that lay people distinguish between such false (...) explanations and to a higher degree classify as lies those explanations that are made with the intention to deceive. Non-deceptionists fail to distinguish between such cases and wrongly classify both as lies. This novel empirical finding indicates the need for supplementing non-deceptionist definitions of lying, at least in some cases, with an additional condition, such as an intention to deceive. (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)

This paper develops an account of trustworthy AI. Its central idea is that whether AIs are trustworthy is a matter of whether they live up to their function-based obligations. We argue that this account serves to advance the literature in a couple of important ways. First, it serves to provide a rationale for why a range of properties that are widely assumed in the scientific literature, as well as in policy, to be required of trustworthy AI, such as safety, justice, (...) and explainability, are properties (often) instantiated by trustworthy AI. Second, we connect the discussion on trustworthy AI in policy, industry, and the sciences with the philosophical discussion of trustworthiness. We argue that extant accounts of trustworthiness in the philosophy literature cannot make proper sense of trustworthy AI and that our account compares favourably with its competitors on this front. (shrink)

Critical notice of Christoph Kelp and Mona Simion's _Sharing Knowledge: A Functionalist Account of Assertion_.

This chapter reviews empirical research on the rules governing assertion and retraction, with a focus on the normative role of truth. It examines whether truth is required for an assertion to be considered permissible, and whether there is an expectation that speakers retract statements that turn out to be false. Contrary to factive norms (such as the influential “knowledge norm”), empirical data suggests that there is no expectation that speakers only make true assertions. Additionally, contrary to truth-relativist accounts, there is (...) no requirement for speakers to retract statements that are false at the context of assessment. We conclude by suggesting that truth still plays a crucial role in the evaluation of assertions: as a standard for evaluating their success, rather than permissibility. (shrink)

Generalisation, universal knowledge claims, and recommendations within disaster studies are problematic because they lead to miscommunication and the misapplication of actionable knowledge. The consequences and impacts thereof are not often considered by experts; forgone as irrelevant to the academic division of labour. There is a disconnect between expert assertions for disaster risk reduction (DRR) and their practical suitability for laypersons. Experts currently assert independently of the context within which protective action measures (PAMs) are to be used, measures unconnected to the (...) people for whom they are developed. This has knock-on effects for DRR: much expert-generated science and policy remains unused, unimplemented, and sometimes misapplied. I use philosophical accounts of assertion and epistemic blame to highlight the epistemic relationship between experts and laypersons. This relationship includes responsibilities and agreements between epistemic agents. Since multilevel DRR knowledge still transfers top-down from experts to laypersons, if experts impair the epistemic relationship, they can be held epistemically blameworthy. To address the pervasiveness of top-down systems, I analyse the epistemic framings and narratives currently shaping DRR, and more specifically PAMs and campaigns. I deconstruct universal perspectives that dominate the epistemic processes of generating, disseminating, and implementing DRR knowledge and specifically for co-seismic landslide PAMs for Nepal. I argue for more inclusive, contextual, and epistemically responsible DRR. Co-production of knowledge should begin from the standpoints of marginalised persons who may have an epistemic advantage due to their socio-politically marginalised positions. Often these epistemic contributions are left out of DRR efforts because marginalised persons are rarely afforded equal, if any, epistemic agency, which results in epistemic gaps and a large pool of relevant knowledge remaining unincorporated and unused in DRR research and policy. (shrink)

I discuss three understandings of the idea of “Knowledge First Epistemology”, i.e. Timothy Williamson’s suggestion that we should take knowledge as a starting point, rather than trying to analyze it. Some have taken this to be a suggestion about the role of the concept of knowledge, but Williamson also seems to be concerned with intuition-based metaphysics. As an alternative, I develop the idea that knowledge may be a social kind that can be understood through a functional analysis in the tradition (...) of Edward Craig. (shrink)