The Interplay of Data, Models, and Theories in Machine Learning

Abstract This paper discusses the role of data within scientific reasoning and as evidence for theoretical claims, arguing for the idea that data can yield theoretically grounded models and be inferred, predicted, or explained from/by such models. Contrary to Bogen and Woodward’s rejection of data-to-theory and theory-to-data inferences/predictions, we draw upon artificial intelligence as applied to science literature to argue that (a) many models are routinely inferred and predicted from the data and routinely used to infer and predict data, and (b) such models can, at least in some contexts, play the role of theories.