Book_XAI

Project information

This book presents a concise and accessible introduction to Explainable Artificial Intelligence (XAI), a field that has become essential for understanding, auditing, and trusting modern machine learning systems. As predictive models grow in complexity and influence, the need to interpret their decisions becomes fundamental for ethical, legal, and technical reasons. Aimed at students, researchers, and professionals entering data science or artificial intelligence, this text provides the conceptual foundations and methodological structure required to navigate the landscape of model explainability.

Grounded in applied knowledge and supported by established academic literature, the book introduces the motivation behind XAI, clarifies its conceptual framework, and presents the main families of methods used to explain machine learning models. Rather than offering an exhaustive catalog of algorithms, it focuses on the essential principles that enable readers to understand model behavior, assess risks, detect biases, and make informed decisions based on predictive systems.

data science