A naturalistic approach to expert-identification begins by asking, ‘how do novices pick out putative experts?’ Alvin Goldman and Elizabeth Anderson, representing a fairly common approach, consider agents’ psychological biases as well as social situatedness. As good as this is, culture’s role in shaping cognitive mechanisms is neglected. An explanatory framework that works well to accommodate culturally-sensitive mechanisms is Peircean semiotics. His triadic approach holds that signs signify objects to interpreters. Applying the triadic model to expert-identification: novices interpret signs of expertise (...) as pointing to particular experts. The main advantage of the framework is that it is significantly more nuanced than the Goldman-Anderson model in describing how agents identify experts. It explicitly accommodates cultural and agential differences in expert-identification. It also explicitly admits the possibility of rational disagreement in assessment of evidence for expertise. But these advantages come at a cost. Namely, it’s difficult in theory – as well as practice – to make one’s way into the semiotic system of someone from another culture to help guide them away from fraudulent experts. Even so, it’s a trade-off worth making since it organizes relevant details for expert-identification, which is a first step in sketching a better normative theory. (shrink)

We carry out most of our epistemic projects as groups. Networks of individuals work together to identify questions, accumulate evidence, and settle on answers that lie beyond the ken of individual knowers. This is particularly important for controversial issues. And when it comes to ideologically contested issues, groups that are ideologically diverse in their membership are epistemically superior to groups that are ideologically homogenous. That’s because ideologically diverse groups are better at (a) identifying a representative sample of important questions, (b) (...) developing a wider range of potential answers, and (c) evaluating the evidence for and against each option. Awareness of this point produces a competence defeater for the relevant outputs of ideologically homogenous groups: they don’t deserve the high level of trust we often grant them. That, among other things, goes a long way towards justifying the public’s decreased trust in institutions like social networks, journalism, and universities. (shrink)

Deepfakes pose a multi-faceted threat to the acquisition of knowledge. It is widely hoped that technological solutions—in the form of artificially intelligent systems for detecting deepfakes—will help to address this threat. I argue that the prospects for purely technological solutions to the problem of deepfakes are dim. Especially given the evolving nature of the threat, technological solutions cannot be expected to prevent deception at the hands of deepfakes, or to preserve the authority of video footage. Moreover, the success of such (...) technologies depends on institutional trust that is in short supply. Finally, outsourcing the discrimination between the real and the fake to automated, largely opaque systems runs the risk of undermining epistemic autonomy. (shrink)