Explainable Deep Learning: A Field Guide for the Uninitiated

Deep neural networks (DNNs) are an indispensable machine learning tool despite the difficulty of diagnosing what aspects of a model’s input drive its decisions. In countless real-world domains, from legislation and law enforcement to healthcare, such diagnosis is essential to ensure that DNN decisions are driven by aspects appropriate in the context of its use. The development of methods and studies enabling the explanation of a DNN’s decisions has thus blossomed into an active and broad area of research. The field’s complexity is exacerbated by competing definitions of what it means “to explain” the actions of a DNN and to evaluate an approach’s “ability to explain”. This article offers a field guide to…

• UD
  U.S. Department of Defense
• OF
  Ohio Federal Research Network
• MU
  Multidisciplinary University Research Initiative
• L
  Leibniz-Gemeinschaft