درس‌آموزهای تقنینی هوش مصنوعی برای ایران- مطالعه تطبیقی قانون اتحادیه اروپا

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه خط مشی گذاری عمومی دانشکده مدیریت، دانشگاه امام صادق(ع)، تهران، ایران

2 دانشجوی دوره دکتری گروه خط مشی گذاری عمومی دانشکده مدیریت دانشگاه امام صادق (ع)، تهران، ایران

چکیده

در دنیای پرشتاب امروز، هوش مصنوعی به یکی از اصلی‌ترین مسائل حکمرانی تبدیل شده و نیاز به تدوین خط‌مشی‌های متناسب را دوچندان ساخته است. این پژوهش توصیفی-تحلیلی، با بهره‌گیری از منابع کتابخانه‌ای، به دنبال پاسخ به این پرسش است: «مکانیسم یادگیری چه درس‌هایی را از قانون هوش مصنوعی اتحادیه اروپا برای تدوین چارچوب حقوقی هوش مصنوعی در جمهوری اسلامی ایران ارائه می‌دهد؟» روش‌شناسی پژوهش بر مبنای مکانیسم یادگیری است که از طریق مطالعه تطبیقی قانون اتحادیه اروپا و وضعیت آن در ایران، در پنج گام عملیاتی شده است. یافته‌ها نشان می‌دهد هفت درس‌آموز اصلی برای خط‌مشی‌گذاری در ایران شامل دسته‌بندی ریسک‌محور، شفافیت مسئولیت‌پذیری، تعیین کاربردهای ممنوعه، تدوین الزامات فنی/اخلاقی، تقویت پاسخگویی، نوآوری‌محوری و تأسیس نهادهای نظارتی/اجرایی می‌باشند. از این‌رو، برخی قضایا در ایران غایب یا نیازمند عملیاتی‌سازی بوده و برخی بصورت نسبی وجود دارند. بهره‌گیری از این قضایا و انطباق آن‌ها با مبانی بومی، می‌تواند تدوین چارچوب قانونی کارآمد و مسئولانه هوش مصنوعی در جمهوری اسلامی ایران را فراهم سازد.

کلیدواژه‌ها


عنوان مقاله [English]

Policy Learning for AI Legislation: A Comparative Analysis of the EU Act and Implications for Iran

نویسندگان [English]

  • MohamadReza Atarodi 1
  • Mahdi Saqi 2
1 Assistant Professor ,Public Policy Department, Faculty of Management, Imam Sadiq University (AS), Tehran, Iran.
2 Ph.D. Candidate in Public Administration (Public Policy), Faculty of Islamic Studies and Management, Imam Sadiq University (AS), Tehran, Iran.
چکیده [English]

In today's fast-paced world, Artificial Intelligence has become a primary governance issue, intensifying the need for suitable policymaking. This descriptive-analytical study, utilizing library resources, seeks to answer the: "What lessons can the policy learning mechanism derive from the European Union's AI Act for shaping the legal framework of Artificial Intelligence in the Iran?" The research methodology is based on the policy learning mechanism, operationalized through a comparative study of the EU AI Act and the situation in Iran across five operational steps. The findings reveal seven key lessons for policymaking in Iran, which include risk-based classification, transparent accountability, prohibition of prohibited uses, formulation of technical/ethical requirements, enhanced accountability, innovation-driven regulation, and establishment of independent/efficient regulatory/executive bodies. Consequently, some of these principles are either absent or require operationalization in Iran, while others are partially present. Leveraging these principles and adapting them to indigenous contexts can facilitate the formulation of an effective, comprehensive, and responsible legal framework for AI in the Iran.

کلیدواژه‌ها [English]

  • Artificial Intelligence (AI)
  • Policy Learning
  • Policy Diffusion Theory
  • Regulation
  • EU AI Act
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