Early Warning Systems, Emerging Technologies, and Inductive Risk

Abstract Early Warning Systems (EWS) are a class of instruments that can be used to predict the risk of hazardous events and issue timely alerts. Even though EWS are increasingly used in various contexts, they are not infallible. In particular, given that empirical claims are always underdetermined by the available evidence, there is always the possibility that they are wrong – for EWS, there is always the possibility of false alarms and failed alerts. Philosophers have discussed this possibility as inductive risk, highlighting how both types of errors may lead to significant epistemic and non-epistemic costs. In this paper, we want to emphasise the need to discuss the implications of the use of emerging EWS technologies for inductive risk. While increasing the quantity and quality of data collected and improving the processing algorithms used to extrapolate predictions can hone accuracy, we argue that these technological improvements involve value-laden judgements and a non-neutral stance with respect to inductive errors. We discuss this in contexts as different as medicine and seismic risk management, where the use of big data and machine learning for EWS is rapidly expanding, and we show that it is important to understand the mechanisms by which different technologies contribute to the improvement of EWS.