Michael T. Stuart

AI methods are being touted as a powerful new source of scientific progress. Are they? If so, what kind of progress do they facilitate? To find out, we employed qualitative research methods to explore how space scientists conceive of AI. We show that space scientists are mainly concerned with whether AI can help them solve specific problems, and more generally, to extend their abilities in useful ways. Inspired by our qualitative data, we propose a new account according to which (at least one type of) scientific progress is improving scientific abilities. We differentiate this view from others, address some objections, and show how it flexibly integrates insights from existing work.

This paper presents a new account of pragmatic understanding based on the idea that such understanding requires skills rather than abilities. Specifically, one has pragmatic understanding of an affordance space when one has, and is responsible for having, skills that facilitate the achievement of some aims using that affordance space. In science, having skills counts as having pragmatic understanding when the development of those skills is praiseworthy. Skills are different from abilities at least in the sense that they are task-specific, can be learned, and we have some cognitive control over their deployment. This paper considers how the use of AI in science facilitates or frustrates the achievement of this kind of understanding. I argue that we cannot properly ascribe this kind of understanding to any current or near-future algorithm itself. But there are ways that we can use AI algorithms to increase pragmatic understanding, namely, when we take advantage of their abilities to increase our own skills (as individuals or communities). This can happen when AI features in human-performed science as either a tool or a collaborator.

Scientists imagine for epistemic reasons, and these imaginings can be better or worse. But what does it mean for an imagining to be epistemically better or worse? There are at least three metaepistemological frameworks that offer different answers to this question: epistemological consequentialism, deontic epistemology, and virtue epistemology. This paper presents empirical evidence that scientists adopt each of these different epistemic frameworks with respect to imagination, but argues that the way they do this is best explained if scientists are fundamentally epistemic consequentialists about imagination.

John D. Norton is responsible for a number of influential views in contemporary philosophy of science. This paper will discuss two of them. The material theory of induction claims that inductive arguments are ultimately justified by their material features, not their formal features. Thus, while a deductive argument can be valid irrespective of the content of the propositions that make up the argument, an inductive argument about, say, apples, will be justified (or not) depending on facts about apples. The argument view of thought experiments claims that thought experiments are arguments, and that they function epistemically however arguments do. These two views have generated a great deal of discussion, although there hasn’t been much written about their combination. I argue that despite some interesting harmonies, there is a serious tension between them. I consider several options for easing this tension, before suggesting a set of changes to the argument view that I take to be consistent with Norton’s fundamental philosophical commitments, and which retain what seems intuitively correct about the argument view. These changes require that we move away from a unitary epistemology of thought experiments and towards a more pluralist position.

Sometimes we learn through the use of imagination. The epistemology of imagination asks how this is possible. One barrier to progress on this question has been a lack of agreement on how to characterize imagination; for example, is imagination a mental state, ability, character trait, or cognitive process? This paper argues that we should characterize imagination as a cognitive ability, exercises of which are cognitive processes. Following dual process theories of cognition developed in cognitive science, the set of imaginative processes is then divided into two kinds: one that is unconscious, uncontrolled, and effortless, and another that is conscious, controlled, and effortful. This paper outlines the different epistemological strengths and weaknesses of the two kinds of imaginative process, and argues that a dual process model of imagination helpfully resolves or clarifies issues in the epistemology of imagination and the closely related epistemology of thought experiments.