XAIVIER: Time Series Classifier Verification with Faithful Explainable AI

Ensuring that a machine learning model performs as intended is a critical step before it can be used in practice. This is commonly done by measuring a model’s predictive performance (e.g., accuracy). However, in high-stakes settings it is often necessary to verify on which data aspects the model actually relies on. This demo presents XAIVIER, the eXplainable AI VIsual Explorer and Recommender, a web application for interactive XAI on time series data. XAIVIER supports dataset exploration and model inspection, allowing users to explain model predictions using various explainer methods. An explainer recommender is provided to advise users which explainer delivers most faithful explanations for their dataset and model. Finally, explanation-based grouping is provided to reveal the model’s underlying decision-making strategies. The proposed set of features aims to cover the full model verification use case for time series classifiers. A demo of XAIVIER is available at https://xai-explorer-demo.know-center.at