Landscape of R packages for eXplainable Artificial Intelligence

TL;DR

This paper reviews R packages for explainable AI, providing a taxonomy, comparing 27 tools, demonstrating an application, and discussing recent trends in the field, with insights on integrating Python libraries.

Contribution

It offers a comprehensive taxonomy and comparison of R packages for XAI, including practical application examples and trend analysis, filling a gap in accessible XAI tool evaluation.

Findings

27 R packages for XAI analyzed and compared

An example application demonstrating package use

Discussion of recent XAI trends

Abstract

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.