Lucyde: A Demonstrator for Explainable Artificial Intelligence and Interactive Machine Learning

Abstract

As AI‑based decision‑support systems become increasingly widespread, methods aimed at improving the performance of human-AI teams are gaining attention. In recent years, explainable artificial intelligence (XAI) has received growing interest, as it provides methods to make the behavior of machine learning models more transparent and can help to identify errors and flaws, which is particularly important in safety critical domains such as medicine and law. However, explanations themselves can be misleading, inconsistent, or incorrect, which makes it essential to raise awareness of the possibilities and limitations of these methods.

We introduce Lucyde, a web‑based demonstrator designed to help users explore, compare, and better understand XAI methods across different datasets, models, and configurations. Lucyde provides a curated collection of explanation techniques, enables side‑by‑side comparison of methods, and offers easy‑to‑understand supplementary information for different user groups. It also illustrates interactive machine learning workflows by allowing users to correct model outputs or explanations. Lucyde thereby fosters informed and reflective engagement with AI systems and their explanations.