Task and Explanation Network
Moshe Sipper

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.
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Scientific Computing and Data Management · Machine Learning in Healthcare
