AI Forensics provides the first comprehensive framework for investigating artificial intelligence systems when they fail, cause harm, or become subjects of legal or regulatory scrutiny. As AI systems power critical decisions in healthcare, finance, autonomous vehicles, and public safety, traditional digital forensics techniques prove inadequate for understanding their complex, opaque, and dynamic behaviors.

You'll master systematic approaches for evidence collection across distributed cloud environments, training data analysis for bias detection and intellectual property violations, model parameter examination to identify tampering or discrimination, and output analysis to validate system performance and detect adversarial attacks. The book provides detailed investigation templates, Python code examples, and statistical validation techniques that can be immediately applied to active cases. Each chapter builds upon previous techniques, creating an integrated investigation framework that scales from small regional deployments to enterprise systems spanning multiple jurisdictions. Through real-world case studies spanning healthcare bias investigations, financial fraud detection failures, and autonomous system malfunctions, you'll learn to answer critical questions: Was the AI system modified after deployment? Does training data contain unauthorized content? Are there signs of model tampering? Does system behavior match specifications?

This practical guide equips forensic investigators, legal professionals, and AI practitioners with specialized methodologies tailored to AI's unique characteristics. It provides both technical depth for expert practitioners and accessible explanations for legal professionals who must understand and present AI evidence in court proceedings.