The generality problem is one of the most pressing challenges for process reliabilism about justification. Thus far, one of the more promising responses is James Beebe’s tri-level statistical solution. Despite the initial plausibility of Beebe’s approach, the tri-level statistical solution has been shown to generate implausible justification verdicts on a variety of cases. Recently, Samuel Kampa has offered a new statistical solution to the generality problem. Kampa argues that the new statistical solution overcomes the challenges that undermined Beebe’s original statistical (...) solution. However, there’s good reason to believe that Kampa is mistaken. In this paper, I show that Kampa’s new statistical solution faces problems that are no less serious than the original objections to Beebe’s solution. Depending on how we interpret Kampa’s proposal, the new statistical solution either types belief-forming processes far too narrowly, or the new statistical solution fails to clarify the epistemic implications of reliabilism altogether. Either way, the new statistical solution fails to make substantive progress towards solving the generality problem. (shrink)

Process reliabilist accounts claim that a belief is justified when it is the result of a reliable belief-forming process. Yet over what range of possible token processes is this reliability calculated? I argue against the idea that _all_ possible token processes (in the actual world, or some other subset of possible worlds) are to be considered using the case of a user acquiring beliefs based on the output of an AI system, which is typically reliable for a substantial local range (...) but unreliable when all possible inputs are considered. I show that existing solutions to the generality problem imply that these cases cannot be solved by a more fine-grained typing of the belief-forming process. Instead, I suggest that reliability is evaluated over a range restricted by the content of the actual belief and by the similarity of the input to the actual input. (shrink)

The global method safety account of knowledge states that an agent’s true belief that p is safe and qualifies as knowledge if and only if it is formed by method M, such that her beliefs in p and her beliefs in relevantly similar propositions formed by M in all nearby worlds are true. This paper argues that global method safety is too restrictive. First, the agent may not know relevantly similar propositions via M because the belief that p is the (...) only possible outcome of M. Second, there are cases where there is a fine-grained belief that is unsafe and a relevantly similar coarse-grained belief that is safe and where both beliefs are based on the same method M. Third, the reliability of conditional reasoning, a basic belief-forming method, seems to be sensitive to fine-grained contents, as suggested by the wide variation in success rates for thematic versions of the Wason selection task. (shrink)

There is a clean image of knowledge transmission between thinkers that involves sincere and reliable speakers, and hearers who carefully assess the epistemic credentials of the testimony that they hear. There is, however, a murkier side to testimonial exchange where deception and lies hold sway. Such mendacity leads to sceptical worries and to discussion of epistemic vice. Here, though, I explore cases where deceit and lies are involved in knowledge transmission. This may sound surprising or even incoherent since lying usually (...) involves saying something that is false. Even if a liar unwittingly tells the truth, those believing their testimony would not acquire knowledge. In §1, however, I suggest a range of examples where lies can be used to transmit knowledge. The kinds of cases upon which I focus are those involving ‘engineered knowledge’, where a speaker skilfully manipulates another into having a true belief. Such cases are epistemologically significant for several reasons. First, they are counter-examples to a range of widely held assumptions about knowledge. We have, for example, cases where sincerity is not necessary for knowledge transmission, where there is knowledge from falsehood, and, as we shall discuss in §2, knowledge that is fragile or not safe. Second, in §3 and §4 I suggest that engineered knowledge is best captured by a virtue theoretic approach to testimonial transmission. (shrink)