Conveners
Contributed session: 4
- Heike Trautmann (Paderborn University)
Machine learning models are often the basis of current automated systems. Trust in an automated system is typically justified only up to a certain degree: A moderately reliable system deserves less trust than a highly reliable one. Ideally, trust is calibrated, in the sense that a human interacting with a system neither over- nor undertrusts the system. To be able to relate objective measures...
Machine learning (ML) will play an increasingly important role in many processes of insurance companies in the future [1]. However, ML models are at risk of being attacked and manipulated [2]. In this work, the robustness of Gradient Boosted Decision Tree (GBDT) models and Deep Neural Networks (DNN) in an insurance context is evaluated. It is analyzed how vulnerable each model is against...