The availability of easy-to-use and reliable software implementations is important for allowing researchers in academia and industry to test, assess and take into use eXplainable AI (XAI) methods. This paper describes the \texttt{py-ciu} Python implementation of the Contextual Importance and Utility (CIU) model-agnostic, post-hoc explanation method and illustrates capabilities of CIU that go beyond the current state-of-the-art that could be useful for XAI practitioners in general.
@article{arxiv.2408.09957,
title = {Contextual Importance and Utility in Python: New Functionality and Insights with the py-ciu Package},
author = {Kary Främling},
journal= {arXiv preprint arXiv:2408.09957},
year = {2024}
}
Comments
In Proceedings of XAI 2024 Workshop of 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), Jeju, South Corea