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Conformal Prediction for Resource Prioritisation in Predicting Rare and Dangerous Outcomes
Varun Babbar · Umang Bhatt · Miri Zilka · Adrian Weller

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.

Author Information

Varun Babbar
Umang Bhatt (University of Cambridge)
Miri Zilka (University of Cambridge)
Adrian Weller (Cambridge, Alan Turing Institute)

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading work on safe and ethical AI. He is a Principal Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.

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