In response to several articles on SERRC, I argue that the common pejorative use of the phrase “conspiracy theory” is the fundamental basis for the distinction between generalism and particularism. That is, generalism describes the “conventional wisdom” about conspiracy theories to which particularism is the corrective. Generalism is best understood as the idea that conspiracy theories ought to be dismissed (perhaps even ridiculed) because they are conspiracy theories--for that is the conventional wisdom (as Charles Pigden has maintained). This is not (...) merely to say, vaguely, that such theories have "epistemic defects." Given this definition, particularism, as the rejection of generalism, is true for any definition of "conspiracy theory" for which Watergate would have at one time counted as one--not just for a minimal definition. Rather than being dismissed based on features that apply to conspiracy theories generally (as generalism would have it), each conspiracy theory should be judged on a case-by-case basis according to its own particular merits and demerits. (shrink)

Matthew Shields (2022) argues that “generalists” regarding conspiracy theories make a mistake: they focus uncritically on what he calls “Non-Dominant Institution Conspiracy Theories” (Non-DITs). He argues that generalists should include “Dominant Institution Conspiracy Theories” (DITs) as paradigmatic cases of conspiracy theories because they are by their own lights the clearest representatives of their views. In my response, I argue that Shields’s conclusion does not necessarily follow. Before we can answer the question of whether generalists should include DITs as paradigmatic cases (...) of conspiracy theories, we need to clarify what conceptual project generalists are pursuing. I argue that the inclusion of DITs depends on the conceptual project one pursues and that the inclusion of DITs makes sense for some projects but not for others. Furthermore, I argue that the difference between DITs and Non-DITs may not be as stark as Shields suggests if we distinguish between the producers and the recipients of conspiracy theories. (shrink)

Janette Dinishak’s work has helped shed critical light on the scientifically questionable and ethically troubling tendency in psychology and philosophy of mind to theorize autistic people as deficient empathizers. In a recently published reply on the Social Epistemology Review and Reply Collective, Dinishak (2024) brings her important perspective on this topic to bear on our paper “AAC Technology, Autism, and the Empathic Turn” (2022). Dinishak is largely sympathetic to our view while also raising a number of rich and thoughtful philosophical (...) questions. Since each of these questions is capable of jumpstarting a lengthy exchange, we focus our response on those questions that, we hope, are particularly fruitful for advancing further conversation. With this aim in mind, we also engage with some of Dinishak’s own insights from her 2016 chapter “Empathy, Like-mindedness, and Autism.” … [please read below the rest of the article]. (shrink)

Whether we ought to trust the testimony of another person is often thought to primarily depend on features like their evidence, knowledge, or level of expertise. No doubt these are epistemically relevant features. However, a recent paper by Justin P. McBrayer (2024) convincingly argues that a testifier's interests may be more important than their expertise when it comes to how we should allocate our epistemic trust in them. Just think of the proverbial used car salesman. You should not trust what (...) he tells you, and realizing that he is an expert only seems to make things worse. In this reply, I consider McBrayer’s basic insight, constructively criticize his view, and tentatively explore further territory. (shrink)

Axel A. Barceló takes issue with my discussion of the dominant gender conception—according to which “woman” is the classical negation of “man”—as an example of logic-based hermeneutical injustice. His arguments are embedded in a more general critique of revisionist projects within feminist logic. [...] Barceló’s particular response relies on a number of assumptions which I think are worth pushing back against. In particular, I will argue that feminist logical revisionism does not depend on giving up universality or proving classical logical (...) truths wrong; that feminist logical revisionist arguments are not meant to be judged in isolation from the rest of the logic literature; and that exploratory work in non-classical feminist logic stands in no special need of justification. (shrink)

We are thankful to Gerhard Schurz for his response (Schurz 2023) to our paper (Spelda et al. 2023). Spelda et al. (2023) shows how a variant of no-regret learning called meta-induction (Schurz 2008; 2019) can be used for optimal selection from available political alternatives and, as a result, also for increasing voter competence that has come under attack from mis/disinformation. Since our paper takes a first step in applying meta-induction to long-standing issues in Democratic Theory (e.g., the usefulness of jury (...) theorems for justifying democracy), we are grateful to Gerhard Schurz for highlighting some of the conditions that need to be met for the development of practical applications. (shrink)

It has been argued that our currently most satisfactory social epistemology of science can’t account for science that is based on artificial intelligence (AI) because this social epistemology requires trust between scientists that can take full responsibility for the research tools they use, and scientists can’t take full responsibility for the AI tools they use since these systems are epistemically opaque. I think this argument overlooks that much AI-based science can be done without opaque models, and that agents can take (...) full responsibility for the systems they use even if these systems are opaque. Requiring that an agent fully understand how a system works is an untenably strong condition for that agent to take full responsibility for the system and risks absolving AI developers from responsibility for their products. AI-based science need not create trust-related social epistemological problems if we keep in mind that what makes both individual scientists and their use of AI systems trustworthy isn’t full transparency of the internal processing but their adherence to social and institutional norms that ensure that scientific claims can be trusted. (shrink)