Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful – at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap. Comparing two social science case studies to a paradigm example from the natural sciences, we argue that, (...) in addition to explanation, prediction is an important goal of social science – and we identify constraints that impede pure ML prediction from being successful in that field. As a remedy, we outline elements of an integrative modelling approach that combines explanatory models and predictive ML models. (shrink)

According to Aristotle, theory is the highest, rational form of knowledge. But do we need theories at all, given the new possibilities created by artificial intelligence to machine process vast amounts of data? The book is based on a two-day symposium jointly organized by the Robert K. Merton Center for Science Studies and the Gesellschaft für Wissenschaftsforschung Berlin. The symposium focused on three themes: 1) Theorizing and its new media: What kind of theorizing do science podcasts enable? 2) Epistemology and (...) the metaphysics of theory, with some remarks on the philosophy of science, including the role of theory in university teaching; 3) AI and theory, with the final question of theory and the transparency of knowledge organization. The book begins with a reflection by Andrew Abbott on theory in the social sciences. (shrink)