This is an open access book. Statistical machine learning (ML) has triggered a renaissance of
artificial intelligence (AI). While the most successful ML models including Deep Neural
Networks (DNN) have developed better predictivity they have become increasingly complex at
the expense of human interpretability (correlation vs. causality). The field of explainable AI
(xAI) has emerged with the goal of creating tools and models that are both predictive and
interpretable and understandable for humans. Explainable AI is receiving huge interest in the
machine learning and AI research communities across academia industry and government and
there is now an excellent opportunity to push towards successful explainable AI applications.
This volume will help the research community to accelerate this process to promote a more
systematic use of explainable AI to improve models in diverse applications and ultimately to
better understand how current explainable AI methods need to be improved and what kind of
theory of explainable AI is needed. After overviews of current methods and challenges the
editors include chapters that describe new developments in explainable AI. The contributions
are from leading researchers in the field drawn from both academia and industry and many of
the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed
include explainability causability and AI interfaces with humans and the applications
include image processing natural language law fairness and climate science.
