Persuasion in AI-driven science: A case of drug design

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.