# A framework for causal concept-based model explanations

## TL;DR

This paper introduces a framework for explaining AI models using causal concepts, ensuring explanations are both understandable and accurate.

## Contribution

The novelty lies in a causal concept-based framework for XAI that ensures explanations are faithful and interpretable.

## Key findings

- Explanations are generated using the probability of sufficiency of concept interventions.
- A proof-of-concept model demonstrates the framework on the CelebA dataset.
- The framework emphasizes the importance of aligning explanation interpretation with generation context.

## Abstract

This work presents a conceptual framework for causal concept-based post-hoc explainable artificial intelligence (XAI), based on the requirements that explanations for non-interpretable models must be both understandable and faithful to the model being explained. Local and global explanations are generated by calculating the probability of sufficiency of concept interventions. Example explanations are presented, generated with a proof-of-concept model made to explain classifiers trained on the CelebA dataset. Understandability is demonstrated through a clear concept-based vocabulary, subject to an implicit causal interpretation. Fidelity is addressed by highlighting important framework assumptions, stressing that the context of explanation interpretation must align with the context of explanation generation.