According to the safety principle, if one knows that p, one's belief that p could not easily have been false. One problem besetting this principle is the lottery problem – that of explaining why one does not seem to know that one will lose the lottery purely based on probabilistic considerations, prior to the announcement of the lottery result. As Greco points out, it is difficult for a safety theorist to solve this problem, without paying a heavy price. In this (...) paper, I first reject three existing safety-based solutions to the lottery problem, due to Pritchard, Sosa, and Broncano-Berrocal. Failure of these accounts reveals that there is something crucial missing in the safety principle. By way of remedying this, I propose to integrate a safety principle with a condition regarding one's own awareness of (nearby) error-possibilities. The resulting account, as I argue, enjoys a number of theoretical advantages, including its capacity to handle the lottery problem. (shrink)

According to the safety account of knowledge, one knows that p only if one's belief could not easily have been false. An important issue for the account is whether we should only examine the belief in the target proposition when evaluating whether a belief is safe or not. In this paper, it is argued that if we only examine the belief in the target proposition, then the account fails to account for why beliefs in necessary truths could fall short of (...) knowledge. But, if we also examine beliefs in other relevant propositions, then the account fails to preserve epistemic closure. Therefore, the safety account cannot find a safe path between epistemic closure and necessary truths. (shrink)

According to the safety principle, if one knows that p, then one’s belief in p could not easily have been false. In this paper, I pose a dilemma for safety theorists by asking the following question: In evaluating whether or not a belief is safe, must we only examine the error-possibilities of the same belief as formed in the actual world? If ‘yes’, safety meets a familiar objection regarding necessary truths and the objection also extends to contingent propositions. If ‘no’, (...) however, there is no necessity of safety for knowledge. It is argued that this dilemma poses a threat for a number of safety principles from the recent literature. In the end, I draw implications of my arguments for the debate between robust and anti-luck virtue epistemologies. The result is that anti-luck virtue epistemology suffers from the difficulty with the safety principle whereas the robust variant remains intact. (shrink)

Moral debunking arguments are meant to show that, by realist lights, moral beliefs are not explained by moral facts, which in turn is meant to show that they lack some significant counterfactual connection to the moral facts (e.g., safety, sensitivity, reliability). The dominant, “minimalist” response to the arguments—sometimes defended under the heading of “third-factors” or “pre-established harmonies”—involves affirming that moral beliefs enjoy the relevant counterfactual connection while granting that these beliefs are not explained by the moral facts. We show that (...) the minimalist gambit rests on a controversial thesis about epistemic priority: that explanatory concessions derive their epistemic import from what they reveal about counterfactual connections. We then challenge this epistemic priority thesis, which undermines the minimalist response to debunking arguments (in ethics and elsewhere). (shrink)

Knowledge demands more than accuracy: epistemologists are broadly agreed that those who know are non-accidentally right, satisfying some kind of safety condition. However, it is hard to formulate any adequate account of safety, and harder still to explain exactly why we care about it. This paper approaches the problem by looking at a concrete human cognitive capacity, face recognition, to see where epistemic safety shows up in it. Drawing on new models in artificial intelligence, and making a case that human (...) face recognition is relevantly similar, I argue that safety is a natural feature of successful generalization. The concluding section of the paper applies this lesson to the problem of our intuitive recognition of knowledge. (shrink)