Example or Prototype? Learning Concept-Based Explanations in Time-Series

With the continuous increase of deep learning applications in safety critical systems, the need for an interpretable decision-making process has become a priority within the research community. While there are many existing explainable artificial intelligence algorithms, a systematic assessment of the suitability of global explanation methods for different applications is not available. In this paper, we respond to this demand by systematically comparing two existing global concept-based explanation methods with our proposed global, model-agnostic concept-based explanation method for time-series data. This method is based on an autoencoder structure and derives abstract global explanations called "prototypes". The results of a human user study and a quantitative analysis show a superior performance of the proposed method, but also highlight the necessity of tailoring explanation methods to the target audience of machine learning models.