Contextual Importance and Utility in Python: New Functionality and Insights with the py-ciu Package
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Abstract (arXiv)
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 textttpy-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.
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References · 6
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 279
- 2020Explanations of Black-Box Model Predictions by Contextual Importance and Utilitycited by 6
- 2022Contextual Importance and Utility: aTheoretical Foundationcited by 5
- 2023Feature Importance versus Feature Influence and What It Signifies for Explainable AIcited by 3
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