Landscape of R packages for eXplainable Artificial Intelligence
This work provides a practical resource for data scientists and researchers using R to implement XAI methods, though it is incremental as it primarily organizes existing tools rather than introducing new techniques.
The authors surveyed and categorized 27 R packages for eXplainable Artificial Intelligence (XAI), providing a taxonomy of explanation methods and comparing these tools to support model exploration, debugging, and validation in machine learning.
The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need methods for exploration, debugging, and validation. New methods and tools for this purpose are being developed within the eXplainable Artificial Intelligence (XAI) subdomain of machine learning. In this work (1) we present the taxonomy of methods for model explanations, (2) we identify and compare 27 packages available in R to perform XAI analysis, (3) we present an example of an application of particular packages, (4) we acknowledge recent trends in XAI. The article is primarily devoted to the tools available in R, but since it is easy to integrate the Python code, we will also show examples for the most popular libraries from Python.