Epistemic implications of machine learning models in science

Abstract

Machine learning techniques are driving — or soon will be driving — much of scientific research and discovery. Can they function as models similar to more traditional modeling techniques in scientific contexts? Or might they replace models altogether if they deliver sufficient predictive accuracy? These questions cut across at least two types of applications of machine learning models. First, machine learning models are used to study the brain, where neural networks might represent aspects of neural activity. The principal question here is: Can the uses of neural networks provide scientific explanations and models for neuroscience? Second, machine learning models are applied in science more broadly, where representational links are less clear. What are the epistemic implications of machine learning in those areas? Can they replace more traditional scientific models of phenomena, or is mere predictive accuracy sufficient? These two strands are finally brought together to create an overview of the (epistemic) role machine learning can play in scientific modeling.