Towards Dependability Metrics for Neural Networks

Chih-Hong Cheng, Georg Nührenberg, Chung-Hao Huang, Hirotoshi Yasuoka

Published 2018-06-06Version 1

Neural networks and other data engineered models are instrumental in developing automated driving components such as perception or intention prediction. The safety-critical aspect of such a domain makes dependability of neural networks a central concern for long living systems. Hence, it is of great importance to support the development team in evaluating important dependability attributes of the machine learning artifacts during their development process. So far, there is no systematic framework available in which a neural network can be evaluated against these important attributes. In this paper, we address this challenge by proposing eight metrics that characterize the robustness, interpretability, completeness, and correctness of machine learning artifacts, enabling the development team to efficiently identify dependability issues.