دانلود کتاب راهنمای مهندسان هوش مصنوعی برای عبور از قانون هوش مصنوعی اتحادیه اروپا

The European Union’s Artificial Intelligence Act, which came into full effect in August 2024, represents a watershed moment in the global regulation of artificial intelligence. As the first comprehensive legal framework for AI, its stated purpose is clear: to foster innovation and development within the EU while effectively mitigating the potential risks posed by AI systems. This ambitious regulation establishes a uniform legal framework governing the development, placement on the market, putting into service, and use of AI systems across the EU. Navigating the complexities of the EU AI Act might, at first glance, seem like a task exclusively for legal teams and policy experts. The Act is indeed complex, featuring 113 articles that address highly technical issues, complemented by 13 annexes that detail implementation specifics and 180 recitals (introductory statements providing context and guidance for interpretation). However, as the practical requirements of the EU AI Act reveal, achieving and maintaining compliance with the Act is fundamentally an engineering problem. This is the core message of this book. Operationalizing EU AI Act compliance goes far beyond legal interpretation. It requires establishing roles, processes, structures, and AI engineering practices. Postmarket compliance, for instance, directly necessitates the implementation of machine learning operations (MLOps) practices such as monitoring and alerting. Successfully achieving EU AI Act compliance is linked to understanding the design, development, and maintenance of AI systems. Compliance is not a legal stamp applied at the end of the development lifecycle, but a continuous process that must be engineered into the core of AI systems from the beginning. The Act’s foundation on the concept of “trustworthy AI” mandates that systems be lawful, ethical, and robust throughout their entire lifecycle. This necessitates embedding ethical and compliance aspects directly into the AI system development process. This book serves as your guide to tackling EU AI Act compliance as the engineering challenge it is. We’ll explore various practical methodologies and frameworks essential for this task, including: AI engineering Defined as the application of software engineering principles to the end-to-end lifecycle of AI systems— including design, development, deployment, and maintenance. CRISP-ML(Q) This structured machine learning development process provides a blueprint for designing, developing, and maintaining AI systems with compliance in mind. Its emphasis on quality assurance and continuous risk management throughout the AI lifecycle is directly aligned with the EU AI Act’s risk-based approach. CRISP-ML(Q) requires documentation of the entire development process, including risk management measures, which is crucial for meeting the Act’s technical documentation and transparency obligations.