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Internal policies, content safety guidelines, and ethical design principles governing system creation.
Also in Engineering & Development
Reasoning
Establishes formal safety policies and guidelines governing general user interactions with AI system.
Users should raise their awareness of the potential safety risks associated with AI products, and select AI products from reputable providers.
2.4.4 Training & AwarenessReview AI product before use
Before using an AI product, users should carefully review the contract or service terms to understand its functions, limitations, and privacy policies. Users should accurately recognize the limitations of AI products in making judgments and decisions, and set reasonable expectations
2.4.4 Training & AwarenessUsers should enhance awareness of personal information protection and avoid entering sensitive information unnecessarily.
2.4.4 Training & AwarenessUsers should be informed about data processing practices and avoid using products that are not in conformity with privacy principles.
2.1.3 Policies & ProceduresUsers should be mindful of cybersecurity risks when using AI products to prevent them from becoming targets of cyberattacks.
2.3.2 Access & Security ControlsUsers should be aware of the potential impact of AI products on minors and take steps to prevent addiction and excessive use.
2.4.2 Design StandardsTechnological measures to address risks
Responding to the above risks, AI developers, service providers, and system users should prevent risks by taking technological measures in the fields of training data, computing infrastructures, models and algorithms, product services, and application scenarios.
1 AI SystemTechnological measures to address risks > Addressing AI’s inherent safety risks
99 OtherTechnological measures to address risks > Addressing safety risks in AI applications
99 OtherComprehensive governance measures
2.1 Oversight & AccountabilityComprehensive governance measures > Implement a tiered and category-based management for AI application
We should classify and grade AI systems based on their features, functions, and application scenarios, and set up a testing and assessment system based on AI risk levels. We should bolster enduse management of AI, and impose requirements on the adoption of AI technologies by specific users and in specific scenarios, thereby preventing AI system abuse. We should register AI systems whose computing and reasoning capacities have reached a certain threshold or those are applied in specific industries and sectors, and demand that such systems possess the safety protection capacity throughout the life cycle including design, R&D, testing, deployment, utilization, and maintenance.
3.1.4 Compliance RequirementsComprehensive governance measures > Develop a traceability management system for AI services
We should use digital certificates to label the AI systems serving the public. We should formulate and introduce standards and regulations on AI output labeling, and clarify requirements for explicit and implicit labels throughout key stages including creation sources, transmission paths, and distribution channels, with a view to enable users to identify and judge information sources and credibility.
3.1.4 Compliance RequirementsAI Safety Governance Framework
National Technical Committee 260 on Cybersecurity of SAC (2024)
Artificial Intelligence (AI), a new area of human development, presents significant opportunities to the world while posing various risks and challenges. Upholding a people-centered approach and adhering to the principle of developing AI for good, this framework has been formulated to implement the Global AI Governance Initiative and promote consensus and coordinated efforts on AI safety governance among governments, international organizations, companies, research institutes, civil organizations, and individuals, aiming to effectively prevent and defuse AI safety risks.
Deploy
Releasing the AI system into a production environment
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management
