# Contextual Importance and Utility in Python: New Functionality and Insights with the py-ciu Package

arXiv: https://arxiv.org/abs/2408.09957 · HTML: https://mlsanity.com/paper/2408.09957/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Explanations of Black-Box Model Predictions by Contextual Importance and Utility](https://mlsanity.com/paper/2006.00199/) (2020)
- [Contextual Importance and Utility: aTheoretical Foundation](https://mlsanity.com/paper/2202.07292/) (2022)
- [Feature Importance versus Feature Influence and What It Signifies for Explainable AI](https://mlsanity.com/paper/2308.03589/) (2023)

## 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.