Oscar Westerblad

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.

While it is uncontroversial that science has been making steady progress, it is controversial what constitutes such progress, among other things, because it is controversial what science’s aims are and what it means for science to have aims in the first place. In this paper, I scrutinize the widespread assumption that scientific progress must be defined in terms of aims. I argue that the notion of progress is not inherently a goal-relative concept. We can define what constitutes progress in science without determining its aims.

Among much else, Peirce was concerned with understanding the nature of experimentation. And while Peirce scholarship frequently appeals to the role of experimentation in his philosophy, no one has yet provided the account appealed to. This is not because Peirce's understanding of experimentation is too obvious to deserve discussion, nor is it because Peirce was quiet on the subject. Indeed, Peirce attempted to articulate what he meant by an experiment in many ways over the course of decades. But these articulations have received limited attention and have not been brought together with the aim of understanding Peircean experimentation. Notably, two of the key sources—Peirce's writings on Petrus Peregrinus de Maricourt and his many drafts of "What Pragmatism Is"—have received little critical engagement. This essay has two aims. The first is to lay out and bring together Peirce's various discussions on the nature of experimentation, providing a sketch of the whole picture. The second is to draw attention to three aspects of this sketch—(1) that experimental inquiry is interest-based, (2) that abduction occurs at key stages throughout, not merely in the production of hypotheses, and (3) that experimental inquiry clarifies and distinguishes two roles for doubt: as a skeptical attitude held towards a hypothesis, and as an uncomfortable state caused by surprise, with only the first sense being crucial.

What is lost if we don’t have a diagnosis? This article examines the aims of clinical medicine and the role of understanding in these aims. Starting from a case prompt with a patient suffering from persistent physical symptoms, I argue that understanding is at the clinical core and that the target of such understanding is the patient’s body with symptoms. Synthesizing accounts of medical understanding and phenomenology of illness, I suggest that the understanding sought in the clinic extends beyond mechanistic explanation to include a sense of bodily intelligibility and that diagnoses are useful but not necessary tools to this end.

In this chapter, I reconstruct and interpret Mary Hesse’s work on scientific language from 1952–1961. I begin by looking at her criticisms of views that hold theoretical concepts and statements are made meaningful by reduction or translation into phenomenal concepts and statements. The shortcomings Hesse identifies with these views inform her positive account of scientific language, which pays close attention to the ways scientists talk about and use models. I highlight three aspects of Hesse's account: (1) her use of Friedrich Waismann’s notion of open texture; (2) the role she thinks models drawn from more familiar experience play in making theories intelligible; and (3) her claim that the relationship between the theoretical and phenomenal statements of our theories involves interpretation rather than translation. I then offer preliminary remarks about Hesse’s use of history in developing this account. Finally, I close with a very brief suggestion about the continuity between Hesse’s early use of open texture and the later parallels she draws between metaphors and models.

What sort of inquiry is social science? This question used to preoccupy philosophers, but fell off their agenda due to a stalemate between so-called naturalists, who took the ideal to benatural science, and exceptionalists, who allied social sciences with the humanities. I show that both positions commit the error of contrastivism, namely, defining social science in contrast to these two traditions, which inevitably ends up caricaturing them. Using recent advances in philosophy, I formulate constructivism about social sciences, a view that denies an essence to this inquiry and grounds it in the needs of communities to understand and improve themselves.

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.