Towards a Rigorous Evaluation of XAI Methods on Time Series

Understand

Explainable Artificial Intelligence (XAI) methods are typically deployed to explain and debug black-box machine learning models.

• However, most proposed XAI methods are black-boxes themselves and designed for images.
• Thus, they rely on visual interpretability to evaluate and prove explanations.
• In this work, we apply XAI methods previously used in the image and text-domain on time series.