AI Protocols (eBook, ePUB)
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AI Protocols provides a comprehensive exploration of the frameworks, standards, and guidelines that govern the development, deployment, and regulation of artificial intelligence systems. The book begins by establishing foundational concepts, highlighting the critical role of protocols in ensuring AI fairness, transparency, accountability, safety, and human oversight throughout the AI lifecycle-from data collection to system retirement.It then delves into technical protocols and standards, covering data governance, model development, rigorous testing, validation, and deployment processes. Empha...
AI Protocols provides a comprehensive exploration of the frameworks, standards, and guidelines that govern the development, deployment, and regulation of artificial intelligence systems. The book begins by establishing foundational concepts, highlighting the critical role of protocols in ensuring AI fairness, transparency, accountability, safety, and human oversight throughout the AI lifecycle-from data collection to system retirement.
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.
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.
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