Conformal Prediction for Resource Prioritisation in Predicting Rare and Dangerous Outcomes

In a growing number of high-stakes decision-making scenarios, experts are aided by recommendations from machine learning (ML) models. However, predicting rare but dangerous outcomes can prove challenging for both humans and machines. Here we simulate a setting where ML models help law enforcement prioritise human effort in monitoring individuals undergoing radicalisation. We discuss the utility of set-valued predictions in guaranteeing the maximal rate at which dangerous radicalized individuals are missed by an assisted decision-making system. We demonstrate the trade-off between risk and the required human effort. We show that set-valued predictions can help better allocate resources whilst controlling the number of high-risk individuals missed. This work explores using conformal prediction and more general risk control methods for assisting in predicting rare and critical outcomes, and developing methods for more expert-aligned prediction sets.