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AI Systems for Remote Biometric Identification
AI Systems for Remote Biometric Identification
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How can AI-driven biometric remote identification systems be deployed in a legally compliant way? This work offers readers structured guidance at the intersection of the AI Act and the GDPR, demonstrating how both regimes can be combined into a coherent assessment framework. Based on a doctrinal legal analysis, it examines the risks of biometric data processing, clarifies key concepts, and defines obligations for providers of AI systems. Case studies illustrate practical challenges and regulato…
  • Leidėjas:
  • Metai: 2026
  • Puslapiai: 86
  • ISBN-10: 368900618X
  • ISBN-13: 9783689006181
  • Kalba: Anglų

AI Systems for Remote Biometric Identification (el. knyga) (skaityta knyga) | knygos.lt

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How can AI-driven biometric remote identification systems be deployed in a legally compliant way? This work offers readers structured guidance at the intersection of the AI Act and the GDPR, demonstrating how both regimes can be combined into a coherent assessment framework. Based on a doctrinal legal analysis, it examines the risks of biometric data processing, clarifies key concepts, and defines obligations for providers of AI systems. Case studies illustrate practical challenges and regulatory gaps. The study combines academic depth with concrete recommendations for practice and regulation. The author's research focuses on IT law and data protection, with a particular emphasis on AI regulation.

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  • Autorius: Bösselmann
  • Leidėjas:
  • Metai: 2026
  • Puslapiai: 86
  • ISBN-10: 368900618X
  • ISBN-13: 9783689006181
  • Kalba: Anglų

How can AI-driven biometric remote identification systems be deployed in a legally compliant way? This work offers readers structured guidance at the intersection of the AI Act and the GDPR, demonstrating how both regimes can be combined into a coherent assessment framework. Based on a doctrinal legal analysis, it examines the risks of biometric data processing, clarifies key concepts, and defines obligations for providers of AI systems. Case studies illustrate practical challenges and regulatory gaps. The study combines academic depth with concrete recommendations for practice and regulation. The author's research focuses on IT law and data protection, with a particular emphasis on AI regulation.

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