AI Protocols

It then delves into technical protocols and standards, covering data governance, model development, rigorous testing, validation, and deployment processes. Emphasis is placed on securing data provenance, mitigating biases, ensuring explainability, reproducibility, and continuous monitoring to maintain robust and reliable AI operations.

The book further examines security, ethics, and compliance challenges, including threat modeling, privacy protection, consent, and adherence to global regulations such as GDPR and the EU AI Act. Ethical AI practices and cross-border compliance are explored in depth to underscore the importance of responsible AI governance.

Sector-specific protocols are detailed for critical domains like healthcare, finance, autonomous systems, and public sector applications, addressing unique validation, risk management, and transparency requirements.

Finally, the book looks ahead to the future of AI protocols, discussing global harmonization efforts, military and dual-use AI considerations, infrastructure governance, and the transformative potential of self-regulating and adaptive AI systems. It highlights the evolving interplay between human and machine roles in shaping AI governance and stresses the need for ongoing collaboration, education, and innovation to navigate the complexities of AI’s expanding impact on society.