Springer Explainable and Interpretable Models in Machine Learning

As computer vision and pattern recognition techniques reach near-human performance, the need to understand how these models function becomes a priority. This book from the Springer Series on Challenges in Machine Learning addresses the fundamental gap between high-performance modeling and model transparency. This collection compiles leading research focused on developing explainable and interpretable methods. It addresses the core questions facing modern AI: what is the rationale behind a specific decision, and how does the model structure explain its own functioning? While high performance is a requirement for modern systems, this text explores how to achieve that performance without sacrificing the ability to interpret results. Designed for researchers and professionals in the field, this work provides a deep look into the development of methods that make machine learning more transparent. It serves as a vital resource for anyone looking to move beyond black-box models toward systems that offer clear, understandable decision-making processes in computer vision and machine learning contexts.