This book is about techniques that can make AI more explainable.
While AI techniques have been spectacularly successful, they are not perfect: their answers are often wrong. To distinguish between great and flawed recommendations, it is desirable to make AI systems explain their recommendations.
This means translating numerical computations—that AI systems perform—into human-understandable natural-language form. It thus makes sense to use existing techniques—e.g., fuzzy—that connect numerical computations with natural language.
The use of fuzzy techniques to make AI explainable is the book's focus. This book contains both theoretical results and applications—to aerospace engineering, agriculture, biology, digital twins, education, law enforcement, medicine, etc. It can be recommended to students and practitioners interested in the state-of-the-art fuzzy-related explainable AI, and to researchers focused on remaining challenges.