Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

The book "Explainable AI: Interpreting, Explaining and Visualizing Deep Learning" offers a comprehensive analysis of the challenges and solutions in the field of explainable artificial intelligence (XAI). In an era where autonomous systems increasingly make decisions, it is crucial that these decisions are understandable and transparent. The work highlights the necessity of maintaining human control and oversight over AI systems, especially in sensitive areas such as healthcare and critical infrastructures. The 22 chapters of the book cover a wide range of topics, including algorithms, theories, and applications of explainable AI techniques. The structured approach to the interpretability of AI models is complemented by discussions of current developments and future directions in this dynamic research field. Readers gain valuable insights into methods for evaluating and explaining AI decisions, which form the foundation for the responsible implementation of AI technologies.