健全人工智能前瞻型治理体系

Core Viewpoint - The article emphasizes the need for modern governance of artificial intelligence (AI) to adapt to its unique characteristics, including self-learning and widespread impact, which necessitates a forward-looking and systematic governance framework [1][6]. Group 1: Current Governance Landscape - Recent efforts in China have focused on improving AI governance rules, particularly in areas like algorithm recommendation and generative AI, establishing clearer boundaries for governance [2]. - Local governments are accelerating the exploration of AI applications in digital governance, public services, and urban management, enhancing efficiency in areas such as regulatory enforcement and city governance [2][3]. - Despite progress, current governance remains reactive, primarily addressing issues after they arise, which limits its effectiveness as AI technology evolves rapidly [3][4]. Group 2: Identified Shortcomings - The existing governance approach is characterized by a delayed response to technological advancements, often intervening only after risks become apparent, leading to increased governance costs and limited flexibility [3][4]. - Governance tools are largely static, relying on policy documents that fail to monitor and adapt to the dynamic nature of AI systems, resulting in challenges in maintaining effective oversight [4]. - Current governance focuses heavily on risk prevention, lacking sufficient guidance on the developmental direction of AI technologies and their alignment with public interests [5]. Group 3: Recommendations for Modernization - To modernize AI governance, it is crucial to integrate governance requirements into the entire lifecycle of AI technology, shifting from reactive to proactive measures [6][7]. - Establishing a risk assessment mechanism that emphasizes forward-looking evaluations is essential to address the rapid evolution and impact of AI technologies [6][7]. - Setting clear operational boundaries and rules for AI systems is necessary to enhance their controllability and accountability from the outset [7][8]. - Governance should not only focus on risk prevention but also actively guide the development direction of AI, aligning it with public interests and enhancing governance effectiveness [8].