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What is Explainable AI for Cyber Risk Management about?

This paper discusses the application of explainable AI methods in cyber risk management, addressing the challenge of interpreting AI predictions in regulated industries. It introduces a novel model that combines rank-based regression with Shapley values, enhanced by statistical normalization using Lorenz Zonoids, to accurately predict and explain ordinal severity levels of cyber risks. The methodology aims to improve predictive accuracy and interpretability, filling a gap in the literature on cyber risk mea

Author

assignments023

Language

EN