About this book:

This book provides a comprehensive guide to the global regulatory landscape for artificial intelligence, emphasizing the transition from abstract ethical principles to concrete organizational practices. It begins by establishing the urgency of AI regulation due to risks like algorithmic bias, lack of accountability, and the "black box" nature of deep learning. The text meticulously breaks down core concepts such as AI systems, models, and risk-based classifications, positioning the EU AI Act and the GDPR as foundational pillars for governance. By categorizing AI systems by risk level—from unacceptable to minimal—the book outlines the stringent requirements for high-risk applications, including mandatory risk management, data governance, technical documentation, and human oversight.

The scope of the book extends beyond the European Union, offering a detailed analysis of the sectoral and state-level "patchwork" approach in the United States, as well as the unique regulatory strategies in the UK, Canada, Brazil, India, and China. Specialized chapters address the high-stakes requirements for healthcare, financial services, and employment, highlighting how existing laws like HIPAA and fair lending statutes are being adapted for machine learning. The text also navigates the technical complexities of cross-border data transfers and localization, explaining how international frameworks like the NIST AI RMF and various ISO standards can be used to operationalize compliance across diverse jurisdictions.

Moving into practical implementation, the book details how MLOps (Machine Learning Operations) serves as the engine for compliance through robust versioning, continuous monitoring, and meticulous logging. It emphasizes the importance of transparency tools like Model Cards and System Cards, as well as the necessity of "security by design" through red teaming and model hardening. The final sections focus on building a sustainable corporate governance structure, defining clear accountability roles, and creating an evidence catalog to survive internal and external audits.

Ultimately, the book argues that responsible AI innovation is a quality attribute that should be designed into the product lifecycle from the start. By providing a phased roadmap for building a compliance program, the text offers a strategic blueprint for organizations to navigate emerging laws while maintaining agility. It concludes that a disciplined approach to fairness, safety, and transparency is not merely a legal hurdle, but a competitive advantage that fosters public trust and ensures the long-term viability of AI technologies.

What You'll Find Inside:

• Detailed explanation of the EU AI Act's risk-based approach, scope, definitions, and specific requirements for high-risk AI systems including conformity assessment, CE marking, and post-market monitoring obligations.
• Comprehensive coverage of GDPR foundations for AI, including lawful bases for processing, special categories of data, automated decision-making rights under Article 22, and Data Protection Impact Assessments (DPIAs) as a core compliance mechanism.
• Sector-specific regulatory guidance for high-impact industries: healthcare (HIPAA, SaMD, clinical safety), financial services (fair lending, model risk management), employment (algorithmic hiring tools and anti-discrimination), and consumer/child protection (FTC enforcement, COPPA, dark patterns).
• Global regulatory landscape analysis comparing approaches in UK, Canada, Brazil, India, and China, with practical strategies for managing cross-border data transfers, localization requirements, and divergent regulatory expectations.
• Practical implementation frameworks including NIST AI RMF, ISO/IEC 42001, ISO 23894, data governance best practices, MLOps for compliance (versioning, monitoring, logging), and step-by-step guidance for building sustainable AI compliance programs.

Who's It For:

Legal and product leaders working together who need to navigate the complex global AI regulatory landscape. Legal teams will benefit from structured summaries, scoping questions, and decision tools to determine regulatory applicability and risk, while product, engineering, and data science teams will gain implementation checklists, logging requirements, and patterns for building audit-ready AI systems that balance compliance with innovation.