Task and Explanation Network

TL;DR

The paper introduces TENet, a framework that integrates task execution with explanation generation, emphasizing the importance of explainability in AI systems.

Contribution

It proposes a novel framework that combines task performance and explanation generation within a unified neural network architecture.

Findings

TENet effectively produces task outcomes along with explanations

The framework promotes the integration of explainability into AI systems

It advocates for a paradigm shift towards explainable AI

Abstract

Explainability in deep networks has gained increased importance in recent years. We argue herein that an AI must be tasked not just with a task but also with an explanation of why said task was accomplished as such. We present a basic framework -- Task and Explanation Network (TENet) -- which fully integrates task completion and its explanation. We believe that the field of AI as a whole should insist -- quite emphatically -- on explainability.