This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise them. (...) For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

The problem of fake news and the spread of misinformation has garnered a lot of attention in recent years. The incentives and norms that give rise to the problem, however, are not unique to journalism. Insofar as academics and journalists are working towards the same goal, i.e., publication, they are both under pressures that pervert. This chapter has two aims. First, to integrate conversations in philosophy of science, epistemology, and metaphilosophy to draw out the publishing incentives that promote analogous problems (...) of fake news and misinformation in academic research communities more broadly. Second, to argue for a (partial) solution. This chapter argues that research communities benefit when authors believe what they argue for in print and it warns against attempts to loosen our publication norms to permit publishing without belief. (shrink)

Whenever we communicate, we inevitably have to say one thing before another. This means introducing particularly subtle patterns of salience into our language. In this paper, I introduce ‘order-based salience patterns,’ referring to the ordering of syntactic contents where that ordering, pretheoretically, does not appear to be of consequence. For instance, if one is to describe a colourful scarf, it wouldn’t seem to matter if one were to say it is ‘orange and blue’ or ‘blue and orange.’ Despite their apparent (...) triviality, I argue that order-based salience patterns tend to make the content positioned first more salient – in the sense of attention-grabbing – in a way that can have surprising normative implications. Giving relative salience to gender differences over similarities, for instance, can result in the activation of cognitively accessible beliefs about gender differences. Where those beliefs are epistemically and/or ethically flawed, we can critique the salience pattern that led to them, providing an instrumental way of evaluating those patterns. I suggest that order-based salience patterns can also be evaluated on constitutive grounds; talking about gender differences before similarities might constitute a subtle form of bias. Finally, I reflect on how the apparent triviality of order-based salience patterns in language gives them an insidious strength. (shrink)

What is the relationship between inquiry and epistemology? Are epistemic norms the norms that guide us as inquirers—as agents in the pursuit of knowledge and understanding? Recently, there has been growing support for what I, following Friedman (Philosophical Review 129(4):501–536, 2020), will call the zetetic turn in epistemology, the view that all epistemic norms are norms of inquiry. This paper investigates the prospects of an inquiry-centered approach to epistemology and develops several motivations for resisting it. First, I argue that the (...) norms of inquiry are most plausibly seen as practical, not as distinctively epistemic. Second, I argue that a zetetically-grounded epistemology is unable to properly account for the rationality of belief. It fails to account for cases where intuitively irrational beliefs promote inquiry, or where intuitively rational beliefs are zetetically useless or counterproductive to inquiry. The main upshot is this: there must be a source of epistemic normativity that isn’t ultimately zetetic. (shrink)

According to widely accepted views in metasemantics, the outputs of chatbots and other artificial text generators should be meaningless. They aren’t produced with communicative intentions and the systems producing them are not following linguistic conventions. Nevertheless, chatbots have assumed roles in customer service and healthcare, they are spreading information and disinformation and, in some cases, it may be more rational to trust the outputs of bots than those of our fellow human beings. To account for the epistemic role of chatbots (...) in our society, we need to reconcile these observations. This paper argues that our engagement with chatbots should be understood as a form of prop-oriented make-believe; the outputs of chatbots are literally meaningless but fictionally meaningful. With the make-believe approach, we can understand how chatbots can provide us with knowledge of the world through quasi-testimony while preserving our metasemantic theories. This account also helps to connect the study of chatbots with the epistemology of scientific instruments. (shrink)