Latest Updates in AI Sector: Establishment of the AIGC Labeling Method Project and Release of the Guidelines for Construction of a Comprehensive Standardization System for the National AI Industry



Recently, authorities have sequentially unveiled significant policy and regulatory updates related to the AI sector. The first announcement pertains to the establishment of a compulsory standard concerning the labeling method for AI-generated content (AIGC), officially titled "Cybersecurity Technology - Labeling Method for Content Generated by Artificial Intelligence." The second pertains to the release of the Guidelines for Construction of a Comprehensive Standardization System for the National AI Industry (2024 Edition). It is advisable for companies engaged in the AI industry to diligently track the advancement and enforcement of these documents, and to promptly formulate and implement strategic responses to capitalize on opportunities for growth.

Establishment of the AIGC Labeling Method Project

On June 25, 2024, the Standardization Administration of China (SAC) announced plans to develop and amend 32 mandatory national standards and to translate 10 mandatory national standards into multiple foreign languages. A key highlight of this announcement was the introduction of a new standard project titled "Cybersecurity Technology - Labeling Method for Content Generated by Artificial Intelligence" (hereinafter referred to as "AIGC Labeling Method") This project will be led by the National Technology Committee on Cybersecurity of Standardization Administration of China (TC260). 

This initiative follows the earlier release of the Cybersecurity Practices Guidelines - Labeling Method for Content Generated by Artificial Intelligence (TC260-PG-20233A) by TC260. However, since the Guideline was not mandatory, its adoption among AIGC companies has been incomplete. In contrast, the forthcoming AIGC Labeling Method will be a compulsory standard, legally enforceable across all AIGC service providers. According to Article 25 of the Standardization Law of the PRC, products and services that do not meet mandatory standards shall not be manufactured, sold, imported or provided. Therefore, following the official introduction of the AIGC Labeling Method, AIGC companies will be required to ensure that all relevant indicators comply with the specific requirements of this standard before offering their services.

Release of the Guidelines for Construction of a Comprehensive Standardization System for the National AI Industry

On July 2, 2024, the Ministry of Industry and Information Technology (MIIT), the Office of the Central Cyberspace Affairs Commission (CCAC), the National Development and Reform Commission (NDRC), and the Standardization Administration of China (SAC) collectively released the Guidelines for Construction of a Comprehensive Standardization System for the National AI Industry (2024 Edition) (hereinafter referred to as "the Guideline"). The Guideline highlights several critical aspects as follows:

1. General Requirements

The Guideline indicates that, to promote the high-quality development of the AI industry, the following goals are planned to be achieved by 2026:

  • Establish more than 50 national and industry standards to accelerate the formation of a robust standardization system for the AI industry.
  • Implement these standards across over 1,000 companies to significantly bolster the role of standards in fostering enterprise innovation.
  • Contributing to the formulation of over 20 international standards to advance the global integration of the AI industry.


2. Construction Approach

The Guideline delineates a comprehensive approach to constructing the comprehensive standardization system for the AI industry, addressing both the structure and the framework. The structure is divided into seven key components: foundational commonality, foundational supports, key technologies, intelligent products and services, enabling new industrialization, industry applications, and safety/governance. The framework similarly encompasses these areas, ensuring a cohesive and comprehensive standardization strategy.

3. Key Directions

The Guideline proposes the following seven key directions:

  • Foundational Common Standards: Including AI terminology, reference structures, and testing assessments to provide a basis for standard formulation and research.
  • Foundational Support Standards: Covering data services, intelligent chips, computing devices, etc., to provide a technological base for industrial development.
  • Key Technology Standards: Including machine learning, large models, natural language processing, etc., to drive technological innovation and application.
  • Intelligent Products and Services: Setting technical requirements for intelligent robots, digital humans, and other intelligent products.
  • Standards Enabling New Industrialization: Involving research and design, manufacturing, etc., to promote the intelligent transformation of the manufacturing industry.
  • Industry Application Standards: Regulating AI applications in areas such as smart cities and smart agriculture.
  • Safety/Governance Standards: Ensuring the safety and reliability of AI technologies and products.


4. Protective Measures

To support the effective implementation of these standards, the Guideline recommends strategic measures in three key areas:
Organizational Development: Establish and enhance standardization technical organizations within the AI field, integrating resources from industry, academia, research, and practical applications.

Talent Development: Encourage standardization research institutions to develop and attract high-end talent and promote the integration of standardization competencies into the professional capability systems of businesses and educational institutions.

Dissemination and Promotion: Through industry associations and standardization institutions, intensify the training and dissemination of the AI standardization system and its key standards among companies to ensure comprehensive standard compliance throughout all business operations.

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