  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.
