HyeJeong Han

In AI-driven science, there is an empirical asymmetry between AI researchers and experimenters, stemming from their different capabilities to control material resources and generate experimental data. This paper develops an “activity-centric approach to persuasion” by focusing on the case of AI-driven drug design to address the asymmetrical relationship between AI researchers and experimenters. First, AI researchers can emphasise several values to persuade experimenters to conduct epistemic activities for pursuing specific AI models. Second, “annotating” the outputs of AI models effectively facilitates the activities of experimenters. Finally, persuasion in AI-driven science proceeds through a reciprocal iteration of the activities between AI researchers and experimenters, which requires an analysis of the pursuit of an AI model at the organisational level. This activity-centric approach to persuasion would provide a useful framework for analysing interdisciplinary research projects, especially those involving asymmetrical relationships.

In regulatory science, where there is high pressure to deliver results with limited time and resources, determining the point at which no experiment should be conducted is a crucial issue. The significance of such economic considerations makes regulatory science a particularly fruitful field for engaging with Peircean accounts of pursuitworthiness. In this paper, I examine how drug regulatory institutions tackle the problem of selecting experiments, drawing on a case study concerning the development of institutional guidelines for conducting bacterial mutagenicity assays and controlling mutagenic impurities. I argue that economic considerations regarding the cost of experiments have motivated regulatory institutions to shape the assessment of the pursuitworthiness of experiments into explicit and concrete forms of non-pursuitworthiness, specifically by standardising assessment methods, criteria, and their implications. In doing so, I present an economic approach to evidential standards, one that treats economic considerations about the cost of experiments as constitutive of the standards for deciding whether to continue gathering evidence while suspending judgement on hypothesis acceptance or rejection. This study of non-pursuitworthiness contributes to general discussions of the pursuitworthiness of experiments by placing social and material costs at the centre of epistemic discussions.

This dissertation analyses the philosophical concept of pursuitworthiness through a historical investigation of drug design and regulation. By focusing on low-level regularities and the value of economic efficiency within the practices of drug design and regulation, I illuminate various aspects of pursuitworthiness. In doing so, I present accounts of pursuitworthiness that centre on what happens during pursuit practices rather than the potential for theoretical development. I employ the concept of pursuitworthiness as a philosophical tool to narrate the interplay between intellectual development and social settings within drug design and regulation, with a particular focus on the Quantitative Structure-Activity Relationship (QSAR) research community. Additionally, I delve into several aspects of pursuitworthiness, grounded in the historical episodes of the QSAR community. First, I analyse what renders data-driven models lacking the potential for theoretical growth pursuitworthy, drawing insights from the early pioneers’ pursuit of QSAR models. Second, emphasising the importance of persuasion at the organisational level of pursuit, I develop an activity-centric approach to persuasion. Third, through an examination of the regulatory use of QSAR models, I analyse the influence of regulatory institutions on the shaping of pursuitworthiness assessments and argue that the models come to represent experiments rather than phenomena as they are integrated into the regulatory system.

This paper aims to develop an account of the pursuitworthiness of models based on a view of models as epistemic tools. This paper is motivated by the historical question of why, in the 1960s, when many scientists hardly found QSAR models attractive, some pharmaceutical scientists pursued Quantitative Structure–Activity Relationship (QSAR) models despite the lack of potential for theoretical development or empirical success. This paper addresses this question by focusing on how models perform their heuristic functions as epistemic tools rather than as potential theories. I argue that models perform their heuristic function by “constructing” phenomena from data in the sense that they allow the model users who interact with the medium of the models to recognise the phenomena as such. The constructed phenomena assist model users in identifying which conditional hypotheses that are focused on low-level regularities concerning entities such as chemical compounds are more “testworthy,” a concept that links the costs associated with hypothesis testing with the fertility of the hypothesis.