Effective Representation to Capture Collaboration Behaviors between Explainer and User
This work addresses the need for better interpretability in AI systems, particularly for deep learning models, but appears incremental as it builds on existing XAI efforts without specifying novel breakthroughs.
The researchers tackled the problem of making explainable AI (XAI) models more transparent by proposing a generic framework for interacting with them in natural language, aiming to capture collaboration behaviors between explainers and users.
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.