May 11, 2022
To tap the full potential of artificial intelligence, not only do we need to understand the decisions it makes, these insights must also be made applicable. This is the aim of the new book "xxAI - Beyond Explainable AI”, edited by Wojciech Samek, head of the Artificial Intelligence department at the Fraunhofer Heinrich Hertz Institute (HHI), and Klaus-Robert Mueller, professor of machine learning at the Technical University of Berlin (TUB) and director at BIFOLD. The publication is based on a workshop held during the International Conference on Machine Learning in 2020. Co-editors also include AI experts Andreas Holzinger, Randy Goebel, Ruth Fong and Taesep Moon. It is already the second publication by Samek and Mueller.
Following the great resonance of the editors' first book, “Explainable AI: Interpreting, Explaining and Visualizing Deep Learning” (2019), which presented an overview of methods and applications of Explainable AI (XAI) and racked up over 300,000 downloads worldwide, their new publication goes a step further. It provides an overview of current trends and developments in the field of XAI. In one chapter, for example, Samek and Mueller's team shows that XAI concepts and methods developed for explaining classification problems can also be applied to other types of problems. When solving classification problems, the target variables sought are categorical, such as "What color is the traffic light right now, red, yellow, or green?". XAI techniques for solving these problems can help explain problems in unsupervised learning, reinforcement learning, or generative models. Thus, the authors expand the horizons of previous XAI research and provide researchers and developers with a set of new tools that can be used to explain a whole new range of problem types and models.
As the title "Beyond Explainable AI" suggests, the book also highlights solutions regarding the practical application of insights from methodological aspects to make models more robust and efficient. While previous research has focused on the process from AI as a "black box" to explaining its decisions, several chapters in the new book address the next step, toward an improved AI model. Furthermore, other authors reflect on their research not only in their own field of work, but also in the context of society as a whole. They cover a variety of areas that go far beyond classical XAI research. For example, they address the relationships between explainability and fairness, explainability and causality, and legal aspects of explainability. The book is available free of charge here.