面向产业的算法治理研究（2025年）

Core Insights - The report emphasizes the need for a trustworthy algorithm governance system that transitions from compliance-driven to trust-driven approaches, addressing issues such as algorithmic "black boxes," data misuse, and discrimination [1][10][11]. Group 1: Algorithm Overview - Algorithms are becoming a core production factor in the digital economy, reshaping industry forms, labor relations, and social governance [9][15]. - The integration of deep learning algorithms with advanced production factors is driving the development of new productive forces, showcasing significant potential for industrial upgrades and high-quality digital economic growth [15][16]. Group 2: Global Algorithm Regulation - China focuses on comprehensive lifecycle governance, emphasizing personal information protection and user rights through regulations like the Personal Information Protection Law and the Internet Information Service Algorithm Recommendation Management Regulations [23][25]. - The U.S. prioritizes innovation while ensuring individual empowerment and public power constraints, with laws like the Anti-Robot Boss Act and the Preventing Addictive Content Exploitation of Children Act [26][27]. - The EU builds a strong regulatory compliance framework centered on rights, with laws such as the General Data Protection Regulation and the Digital Services Act, ensuring transparency and user rights [28][30]. Group 3: Algorithm Governance Framework - The governance framework consists of three pillars: technology, rules, and platforms, focusing on transparency, information protection, fairness, and content security [10][11][22]. - The report advocates for a shift from a compliance-driven approach to a user-driven trustworthy algorithm practice system, balancing efficiency and fairness [21][22]. Group 4: Challenges in Algorithm Governance - The report identifies multiple challenges in algorithm governance, including the lack of transparency in decision-making processes, privacy risks, and the potential for reinforcing social biases [18][19]. - It highlights the need for a multi-stakeholder governance model that includes government, industry, and public participation to address these challenges effectively [19][31].