The growing demand for transparency, interpretability, and explainability of machine learning models and AI systems has fueled the development of methods aimed at understanding the properties and behavior of such models (XAI). Since different methods answer different explainability questions, it is crucial to understand the kind of explanation the different XAI-methods provide, and in what situations they should be used. We introduce eXplego, an interactive tree-structured tool designed to assist users in selecting the most suitable XAI method for their use case. eXplego prompts users to answer questions regarding the type of explanation they seek, guiding them along the branches of the decision tree for further inquiries. After 2-5 questions, the tree reaches one of its leaves to suggest an XAI method aligned with the user’s explainability need. The tool also provides helpful practical examples, simplified descriptions of the suggested method’s functionality and interpretability, points to consider when using the method, and links to the paper introducing the method, additional resources, and software implementations. The tool is developed from an in-depth study to discern the characteristics of the most prominent methods and the nature of the explanations they provide. We believe eXplego will help streamline the process of XAI method selection and contribute to the practical implementation of XAI in various domains. The tool is available at explego.nr.no.
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