Open science is good for both epistemic and social reasons, but in nonobvious ways, it can have detrimental epistemic side effects. Drawing on case studies and the social epistemology of science, I show how practices intended to increase transparency, communication, and information sharing in science can backfire. We should not reject Open Science, just implement it carefully. I argue that we can do so by treating openness as a governing value in science, and thus, that our pursuit of openness needs to be balanced against our pursuit of the whole scheme of values that govern science.

I raise some questions about Jeremy Fantl's The Limitations of the Open Mind. I ask what type of applied epistemology Fantl's book represents, whether there might be a better conception of open-mindedness than the one he embraces, and whether he is correct that someone's being an amateur makes it easier for their knowledge to survive the dismissal of relevant counterarguments.

I introduce the book symposium on Jeremy Fantl's The Limitations of the Open Mind. The symposium began as a session at the 2023 American Philosophical Association meeting in Montreal; it features replies to Fantl's book by Nathan Ballantyne and Miriam Schleifer McCormick with replies to the replies by Fantl.

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.

According to an argument that I will call the argument from loose lips, we can safely reject certain notorious conspiracy theories because they posit conspiracies that would be nearly impossible to keep secret. I distinguish between three versions of this argument: the epistemic argument, the alethic argument, and the statistical argument. I, then, discuss several limitations of the argument from loose lips. The first limitation is that only the statistical argument can be applied to new conspiracy theories. The second limitation is that no version of the argument suffices to rule out the existence of small initial conspiracies that have no need to add further conspirators. The third limitation is that no version of the argument is dialectically efficacious in the context of arguing with the relevant conspiracy theorists because nothing is said to address the alleged evidence that they cite.

Quassim Cassam has argued that psychological and epistemological analyses of conspiracy theories threaten to overlook the political nature of such theories. According to Cassam, conspiracy theories are a form of political propaganda. I develop a limited critique of Cassam's analysis.

This paper advances two core theses. First, acceptance of conspiracy theories requires a rejection of epistemic authority that renders conspiracy theorists susceptible to co-option by certain political programs while insulating such programs from criticism. I argue that the contrarian nature of conspiracy theories partially accounts for the prominence of such theories in populist movements.

Second, the contrarian nature of conspiracy theories partially accounts for their attractiveness, especially among those to whom populism already appeals. I argue that for those who resent what appears from their perspective as the shaping of the epistemic landscape by alien perspectives, conspiracy theorizing may facilitate the reassertion of epistemic autonomy.

Epistemic dependence refers to our social mechanisms of reliance in practices of knowledge production. Epistemic oppression concerns persistent and unwarranted exclusions from those practices. This article examines the relationship between these two frameworks and demonstrates that attending to their relationship is a fruitful practice for applied epistemology. Paying attention to relations of epistemic dependence and how exclusive they are can help us track epistemically oppressive practices. In order to show this, I introduce a taxonomy of epistemic dependence (interpersonal – communal – structural). I argue that this particular taxonomy is useful for tracking epistemically oppressive practices in institutional contexts. This is because, first, the forms of epistemic dependence in this taxonomy yield, what I call, diagnostic questions. These are questions that help us track how relations of epistemic dependence could become exclusive and that thus help reveal epistemic oppression in institutional contexts. Second, the forms of epistemic dependence in the taxonomy are interrelated. Paying attention not just to each of three forms of epistemic dependence but also to the way in which they are interrelated is useful for illuminating epistemically oppressive practices. I conclude by demonstrating how the diagnostic questions can be used in analyses of concrete institutional practices in asylum law and higher education.

This article elaborates a conceptual framework to systematize the epistemic evaluation of social systems. This framework can be used to structure an evaluation or to characterize and assess existing ones. The article then uses the framework to assess four representative evaluations of think tanks. This meta-evaluation exemplifies how the framework can play its structuring role. It also leads us to general conclusions about the existing evaluations of think tanks. Most importantly, by focusing on the organizational level, existing evaluations miss factors that are situated at the network and ecosystemic levels and that significantly determine how well think tanks serve society in producing and disseminating knowledge relevant to public policy. This conclusion suggests the need for epistemic evaluations of think tank ecosystems.