An explainable AI (XAI) model aims to provide transparency (in the form of justification, explanation, etc) for its predictions or actions made by it. Recently, there has been a lot of focus on building XAI models, especially to provide explanations for understanding and interpreting the predictions made by deep learning models. At UCLA, we propose a generic framework to interact with an XAI model in natural language.
@article{arxiv.2201.03147,
title = {Effective Representation to Capture Collaboration Behaviors between Explainer and User},
author = {Arjun Akula and Song-Chun Zhu},
journal= {arXiv preprint arXiv:2201.03147},
year = {2022}
}