系好人工智能发展“安全带”

Core Insights - The release of the 2.0 version of the "Artificial Intelligence Security Governance Framework" aims to provide clear guidelines for managing AI risks across different industries, enhancing the operability of AI safety governance and contributing to global AI governance with a Chinese solution [1][2] Industry Overview - The AI industry in China has become a significant driver of economic growth, with its scale exceeding 900 billion yuan in 2024, representing a 24% year-on-year increase, and the number of enterprises surpassing 5,300, forming a relatively complete industrial system [1] - AI is profoundly transforming traditional industries, with widespread applications in manufacturing, finance, and healthcare, showcasing significant potential in cost reduction, efficiency enhancement, and resource optimization [1] Risks and Challenges - Despite the benefits, AI also poses risks such as data breaches, model defects, and ethical issues, with approximately 74% of AI-related risk events from 2019 to 2024 directly linked to safety concerns [1] - From June 2024 to July 2025, there were 59 publicly reported safety incidents globally, involving issues like forgery fraud, algorithmic discrimination, and autonomous driving decision errors, highlighting the urgent need for a scientific AI governance system [1] Governance Principles - The governance approach should focus on three key areas: governance principles, risk classification, and collaborative governance [2] - The principle of inclusive prudence emphasizes the need for trustworthy AI that actively prevents uncontrolled risks, ensuring that AI remains under human control and aligns with fundamental human interests [2] Risk Classification - AI risks can be categorized into three types: inherent technical defects, interference during usage (e.g., hacking), and cascading effects (e.g., job market disruption) [2] - Targeted governance measures should be implemented based on risk types, clarifying obligations for stakeholders at each stage [2] Collaborative Governance - A comprehensive governance strategy involves participation from government, enterprises, research institutions, and the public, utilizing regulations, technological safeguards, and ethical guidance for full-chain management of AI [3] - Existing regulatory frameworks, such as the "Interim Measures for the Management of Generative AI Services," and academic proposals like the "AI Model Law 3.0," represent significant steps toward establishing a governance system with Chinese characteristics [3] Conclusion - Ensuring safety is a prerequisite for development, and governance is essential for innovation, as AI safety governance impacts social security, industrial development, and economic growth [4] - A systematic and effective governance framework is necessary for AI to become a safe and vital engine for high-quality economic development [4]