Why epidemiology should not be automated: the case for slow epidemiology

EXTRACT:Bann et al. pose the question, ‘why can’t epidemiology be automated (yet)?’, as though the automation of epidemiology is both inevitable and desirable [1]. They describe a future of ‘1-click reviews’ and ‘end-to-end automation’ that will ‘[boost] the efficiency of current practice and … [create] new opportunities for’. But scientific discovery requires far more than increased efficiency. In epidemiology, new knowledge comes from critical reasoning about data, about context, and about the limits of our existing understanding. Such reasoning, in turn, demands deep thought, scholarship, and diligence. All are threatened by automation.