Description

Implementation of the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). A recent description of CIU can be found in e.g. Främling (2020) <arXiv:2009.13996>.

Details

This package implements the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). CIU allows explaining outputs values of any regression or classification systems, no matter if it is a "black-box" or a "white-box" AI, or anything between black and white. CIU is entirely model-agnostic. Contrary to most (all?) other XAI methods, CIU provides explanations directly based on the observed input-output behavior without building an intermediate "interpretable" model for doing it.

CIU was developed by Kary Främling in his PhD thesis, which was presented in 1996 (in French). CIU was first presented in 1995 at the International Conference on Artificial Neural Networks (ICANN).

The ciu package supports models from caret and at least lda natively, but can easily be made to work with any model.

Main functions:

Use of ciu starts by calling the function ciu.new that returns an object of class CIU. If the ciu object is created by ciu <- ciu.new(...), then different methods can be called as ciu$explain(), ciu$barplot.ciu() etc. for obtaining explanations in different forms.

ciu is implemented using an "old style" (?) R object orientation. However, it provides object-oriented encapsulation of variables and methods of the CIU object, which presumably helps to avoid name conflicts with other packages or user code.

References

Främling, K. Explainable AI without Interpretable Model. 2020, https://arxiv.org/abs/2009.13996.

Främling, K. Decision Theory Meets Explainable AI. 2020, https://doi.org/10.1007/978-3-030-51924-7_4.

Främling, K. Modélisation et apprentissage des préférences par réseaux de neurones pour l'aide à la décision multicritère. 1996, https://tel.archives-ouvertes.fr/tel-00825854/document (title translation in English: Learning and Explaining Preferences with Neural Networks for Multiple Criteria Decision Making)