About this book:

This book provides a comprehensive guide to the "sociotechnical" governance of AI-driven robotics, tracing the shift from deterministic industrial automation to autonomous learning systems. It argues that responsible deployment requires an integrated triad of ethics, law, and policy, moving beyond abstract principles to verifiable engineering practices. By examining the "autonomy continuum," the text illustrates how machine agency complicates traditional legal doctrines, specifically regarding foreseeability, product liability, and the attribution of criminal and civil negligence among manufacturers, integrators, and operators.

The middle chapters delve into the technical requirements for building trustworthy machines, emphasizing "governance by design." This includes the implementation of Explainable AI (XAI) to resolve the "black box" problem, rigorous safety engineering through structured "safety cases," and the mitigation of algorithmic bias to protect human rights and social justice. The book also provides a detailed analysis of the data governance landscape, navigating the complexities of GDPR, biometric privacy laws, and the intellectual property challenges inherent in human-robot collaboration.

On a global scale, the text contrasts the sectoral, innovation-led regulatory approach of the United States with the centralized, risk-based framework of the European Union’s AI Act. It explores domain-specific regulations in healthcare, autonomous transportation, and public surveillance, highlighting the unique ethical stakes in each. Key concerns such as cybersecurity resilience, environmental sustainability, and the impact of automation on labor markets are addressed as critical components of a robot’s total lifecycle management.

The book concludes by offering a practical roadmap for practitioners and policymakers. It advocates for continuous assurance through post-market monitoring, incident reporting, and the use of regulatory sandboxes for policy experimentation. By utilizing provided toolkits and checklists, organizations are encouraged to cultivate an internal culture of responsibility, ensuring that the evolution of intelligent machines remains aligned with human safety, dignity, and democratic accountability.

What You'll Find Inside:

• Comprehensive ethical frameworks for AI robotics covering beneficence, non-maleficence, autonomy, and justice principles with practical guidance on translating values into verifiable system requirements
• Detailed analysis of global regulatory landscapes including the EU AI Act, US sectoral approaches, and international harmonization efforts with specific cross-border compliance navigation strategies
• Practical governance-by-design methodologies featuring safety cases, impact assessments (ethical, data protection, human rights), and continuous assurance mechanisms to build trustworthy intelligent machines
• Sector-specific regulations for healthcare, transportation, public safety, and workplace robotics addressing unique challenges like medical device compliance, autonomous vehicle liability, and algorithmic bias in law enforcement
• Actionable toolkits for companies and policymakers including checklists, decision trees, model clauses, and implementation guidance for responsible AI robotics development and deployment throughout the lifecycle

Who's It For:

This book is designed for practitioners and policymakers working with AI robotics systems, including product leaders and engineers responsible for safe deployment, compliance officers and legal counsel translating requirements into technical controls, risk managers and insurers assessing and mitigating exposure, and public officials balancing innovation with public protection. It provides actionable guidance for anyone involved in the development, deployment, or governance of intelligent machines operating in public and private domains.