Traditional secular conspiracy theories and explanations of worldly events in terms of supernatural agency share interesting epistemic features. This paper explores what can be called “supernatural conspiracy theories”, by considering such supernatural explanations through the lens of recent work on the epistemology of secular conspiracy theories. After considering the similarities and the differences between the two types of theories, the prospects for agnosticism both with respect to secular conspiracy theories and the existence of God are then considered. Arguments regarding secular conspiracy theories suggest ways to defend agnosticism with respect to God from arguments that agnosticism is not a logically stable position and that it ultimately collapses into atheism, as has been argued by N. Russell Hanson and others. I conclude that such attacks on religious agnosticism fail to appreciate the conspiratorial features of God's alleged role in the universe.

We often get evidence concerning the reliability of our own thinking about some particular matter. This “higher-order evidence” can come from the disagreement of others, or from information about our being subject to the effects of drugs, fatigue, emotional ties, implicit biases, etc. This paper examines some pros and cons of two fairly general models for accommodating higher-order evidence. The one that currently seems most promising also turns out to have the consequence that epistemic akrasia should occur more frequently than is sometimes supposed. But it also helps us see why this might not be a bad thing.

This paper argues that judges assessing the scientific validity and the legal admissibility of forensic science techniques ought to privilege testing over explanation. Their evaluation of reliability should be more concerned with whether the technique has been adequately validated by appropriate empirical testing than with whether the expert can offer an adequate description of the methods she uses, or satisfactorily explain her methodology or the theory from which her claims derive. This paper explores these issues within two specific contexts: latent fingerprint examination and the use of breath tests for the detection of alcohol. Especially in the forensic science arena, I suggest courts have often been seduced by superficially plausible explanations and descriptions of a technique or method, and permitted these to serve as a substitute for empirical testing. Thinking through these two examples illustrates both why evaluating the extent of testing should be the most important method by which courts assess reliability, and why, when other forms of explanatory evidence are readily available, we may nonetheless elect to make use of them. This paper suggests that these descriptions and explanations may at times usefully supplement evidence of testing, but should not generally be substituted for it. Finally, this paper embraces a kind of evidentiary pragmatism, in which the quantum of evidence required to establish legal reliability is determined not in the abstract, but in relation to the evidence that is, or ought to be, available as a result of reasonable research and investigation.

According to Selim Berker the prevalence of consequentialism in contemporary epistemology rivals its prevalence in contemporary ethics. Similarly, and more to the point, Berker finds epistemic consequentialism, epitomized by process reliabilism, to be as misguided and problematic as ethical consequentialism. This paper shows how Berker misconstrues process reliabilism and fails to pinpoint any new or substantial defects in it.

Epistemic diversity is widely approved of by social epistemologists. This paper asks, more specifically, how much epistemic diversity, and what kinds of epistemic diversity are normatively appropriate? Both laissez-faire and highly directive approaches to epistemic diversity are rejected in favor of the claim that diversity is a blunt epistemic tool. There are typically a number of different options for adequate diversification. The paper focuses on scientific domains, with particular attention to recent theories of smell.

In this paper I defend a form of epistocracy I call limited epistocracy – rule by institutions housing expertise in non-political areas that become politically relevant. This kind of limited epistocracy, I argue, isn't a far-off fiction. With increasing frequency, governments are outsourcing political power to expert institutions to solve urgent, multidimensional problems because they outperform ordinary democratic decision-making. I consider the objection that limited epistocracy, while more effective than its competitors, lacks a fundamental intrinsic value that its competitors have; namely, political inclusion. After explaining this challenge, I suggest that limited epistocracies can be made compatible with robust political inclusion if specialized institutions are confined to issuing directives that give citizens multiple actionable options. I explain how this safeguards citizens’ inclusion through rational deliberation, choice, and contestation.

Alvin Goldman has criticized the idea that, when evaluating the opinions of experts who disagree, a novice should “go by the numbers”. Although Goldman is right that this is often a bad idea, his argument involves an appeal to a principle, which I call the non-independence principle, which is not in general true. Goldman's formal argument for this principle depends on an illegitimate assumption, and the examples he uses to make it seem intuitively plausible are not convincing. The failure of this principle has significant implications, not only for the issue Goldman is directly addressing, but also for the epistemology of rumors, and for our understanding of the value of epistemic independence. I conclude by using the economics literature on information cascades to highlight an important truth which Goldman's principle gestures toward, and by mounting a qualified defense of the practice of going by the numbers.

The central thesis of robust virtue epistemology (RVE) is that the difference between knowledge and mere true belief is that knowledge involves success that is attributable to a subject's abilities. An influential objection to this approach is that RVE delivers the wrong verdicts in cases of environmental luck. Critics of RVE argue that the view needs to be supplemented with modal anti-luck condition. This particular criticism rests on a number of mistakes about the nature of ability that I shall try to rectify here.

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes.

Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts–Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient.

This paper focuses on the question of how to resolve disagreement and uses the Lehrer-Wagner model as a formal tool for investigating consensual decision-making. The main result consists in a general definition of when agents treat each other as epistemic peers (Kelly 2005; Elga 2007), and a theorem vindicating the “equal weight view” to resolve disagreement among epistemic peers. We apply our findings to an analysis of the impact of social network structures on group deliberation processes, and we demonstrate their stability with the help of numerical simulations.

While it is widely acknowledged that knowledge can be acquired via testimony, it has been argued that understanding cannot. While there is no consensus about what the epistemic relationship of understanding consists in, I argue here that regardless of how understanding is conceived there are kinds of understanding that can be acquired through testimony: easy understanding (e.g. understanding simple and mundane information) and easysunderstanding (e.g. understanding information that might be complex, but is nevertheless easy for an expert S). I address a number of aspects of understanding that might stand in the way of being able to acquire understanding through testimony, focusing on understanding's paradigmatic form and what it means to say that in order to understand something you need to “grasp” some information or the relationship between bits of information. I argue that in cases of both easy and easys understanding, no aspect of understanding stands in the way of it being able to acquire it through testimony. As a result, while not all understanding can be acquired through testimony in all instances and for all subjects, this failure of acquisition is only a product of the complexity of the relevant information or one's unfamiliarity with it, and not a product of the epistemic relationship of understanding.

Can ordinary citizens in a democracy evaluate the claims of scientific experts? While a definitive answer must be case by case, some scholars have offered sharply opposed general answers: a skeptical “no” (e.g. Scott Brewer) versus an optimistic “yes, no problem” (e.g. Elizabeth Anderson). The article addresses this basic conflict, arguing that a satisfactory answer requires a first-order engagement in judging the claims of experts which both skeptics and optimists rule out in taking the issue to be one of second-order assessments only. Having argued that such first-order judgments are necessary, it then considers how they are possible, outlining a range of practices and virtues that can inform their success and likelihood, and drawing throughout on ancient Greek insights as well as contemporary social psychology and sociology of knowledge. In conclusion the ethics of democratic judgment so developed is applied to the dramatic conviction of the members of an Italian scientific risk commission in L'Aquila.

Forensic science error rates are needlessly high. Applying the perspective of veritistic social epistemology to forensic science could produce new institutional designs that would lower forensic error rates. We make such an application through experiments in the laboratory with human subjects. Redundancy is the key to error prevention, discovery, and elimination. In the “monopoly epistemics” characterizing forensics today, one privileged actor is asked to identify the truth. In “democratic epistemics,” several independent parties are asked. In an experiment contrasting them, democratic epistemics reduced the rate at which biased observers obscured the truth by two-thirds. These results highlight, first, the potential of “epistemic systems design,” which employs the techniques of economic systems design to address issues of veracity rather than efficiency, and second, the value of “experimental epistemology,” which employs experimental techniques in the study of science.

In this paper I embrace what Brian Keeley calls in “Of Conspiracy Theories” the absurdist horn of the dilemma for philosophers who criticize such theories. I thus defend the view that there is indeed something deeply epistemically wrong with conspiracy theorizing. My complaint is that conspiracy theories apply intentional explanations to situations that give rise to special problems concerning the elimination of competing intentional explanations.

Permissivism is the view that there are evidential situations that rationally permit more than one attitude toward a proposition. In this paper, I argue for Intrapersonal Belief Permissivism (IaBP): that there are evidential situations in which a single agent can rationally adopt more than one belief-attitude toward a proposition. I give two positive arguments for IaBP; the first involves epistemic supererogation and the second involves doubt. Then, I show how these arguments give intrapersonal permissivists a distinct response to the toggling objection. I conclude that IaBP is a view that philosophers should take seriously.

This paper explores whether brain images may be admitted as evidence in criminal trials under Federal Rule of Evidence 403, which weighs probative value against the danger of being prejudicial, confusing, or misleading to fact finders. The paper summarizes and evaluates recent empirical research relevant to these issues. We argue that currently the probative value of neuroimages for criminal responsibility is minimal, and there is some evidence of their potential to be prejudicial or misleading. We also propose experiments that will directly assess how jurors are influenced by brain images.

Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. I argue for a unified account of these key concepts that treats the concept of understanding as fundamental. This allows resources from the philosophy of science and the epistemology of understanding to help guide opacity alleviation efforts. A first significant benefit of this understanding account is that it defuses one of the primary, in-principle objections to post hoc explainable AI (XAI) methods. This “rationalization objection” argues that XAI methods provide mere rationalizations rather than genuine explanations. This is because XAI methods involve using a separate “explanation” system to approximate the original black box system. These explanation systems function in a completely different way than the original system, yet XAI methods make inferences about the original system based on the behavior of the explanation system. I argue that, if we conceive of XAI methods as idealized scientific models, this rationalization worry is dissolved. Idealized scientific models misrepresent their target phenomena, yet are capable of providing significant and genuine understanding of their targets.

There has been much recent interest in imprecise probabilities, models of belief that allow unsharp or fuzzy credence. There have also been some influential criticisms of this position. Here we argue, chiefly against Elga (2010), that subjective probabilities need not be sharp. The key question is whether the imprecise probabilist can make reasonable sequences of decisions. We argue that she can. We outline Elga's argument and clarify the assumptions he makes and the principles of rationality he is implicitly committed to. We argue that these assumptions are too strong and that rational imprecise choice is possible in the absence of these overly strong conditions.

In recent years, various computational models have been developed for studying the dynamics of belief formation in a population of epistemically interacting agents that try to determine the numerical value of a given parameter. Whereas in those models, agents’ belief states consist of single numerical beliefs, the present paper describes a model that equips agents with richer belief states containing many beliefs that, moreover, are logically interconnected. Correspondingly, the truth the agents are after is a theory (a set of sentences of a given language) rather than a numerical value. The agents epistemically interact with each other and also receive evidence in varying degrees of informativeness about the truth. We use computer simulations to study how fast and accurately such populations as wholes are able to approach the truth under differing combinations of settings of the key parameters of the model, such as the degree of informativeness of the evidence and the weight the agents give to the evidence.

Condorcet's famous jury theorem reaches an optimistic conclusion on the correctness of majority decisions, based on two controversial premises about voters: they are competent and vote independently, in a technical sense. I carefully analyse these premises and show that: (i) whether a premise is justified depends on the notion of probability considered and (ii) none of the notions renders both premises simultaneously justified. Under the perhaps most interesting notions, the independence assumption should be weakened.