On Feature Relevance Uncertainty: A Monte Carlo Dropout Sampling Approach

Understand

Understanding decisions made by neural networks is key for the deployment of intelligent systems in real world applications.

• However, the opaque decision making process of these systems is a disadvantage where interpretability is essential.
• Many feature-based explanation techniques have been introduced over the last few years in the field of machine learning to better understand decisions made by neural networks and have become an important component to verify their reasoning capabilities.
• However, existing methods do not allow statements to be made about the uncertainty regarding a feature's relevance for the prediction.