历史小径·世界史英语精读30篇（3）
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Comparing International Frameworks for Artificial Intelligence Governance
人工智能监管框架的国际比较
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The EU’s AI Act classifies systems by risk level, banning real-time biometric surveillance in public spaces except under strict judicial oversight.
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In contrast, the U.S. adopts sectoral regulation—FDA oversees medical AI, FAA certifies aviation algorithms, and FTC enforces fairness in hiring tools.
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China’s Interim Measures require generative AI providers to obtain security assessments and label synthetic content transparently.
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Japan emphasizes voluntary guidelines aligned with OECD principles, prioritizing human oversight and social trust over binding legislation.
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Canada’s AIDA proposes mandatory impact assessments for high-impact AI, focusing on systemic bias and accountability in deployment contexts.
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These differences reflect deeper constitutional values: EU precaution, U.S. innovation pragmatism, China’s state-led stability, and Japan’s consensus culture.
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Multilateral efforts like the GPAI aim to harmonize metrics for algorithmic transparency, yet lack enforcement mechanisms or shared definitions of ‘harm’.
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Firms operating globally face compliance fragmentation—training data rules in Brazil differ sharply from those in Singapore or Germany.
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Regulatory divergence also shapes R&D investment: EU startups focus on explainability; Chinese labs prioritize multimodal integration within domestic infrastructures.
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Emerging ‘regulatory sandboxes’ in the UK and South Korea allow temporary exemptions to test governance models before formal adoption.
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Ultimately, AI governance reveals how legal traditions, economic structures, and historical experiences continue to define technological sovereignty in the 21st century.