China Provisions on Algorithmic Recommendation (in force 2022-03-01)

Algorithm filing for services with public-opinion attributes or social-mobilisation capacity, security assessment, display of the filing number, and a user option to switch off personalised recommendation

By Jorge García Aibar Reviewed 2026-09-20

From clause to evidence Build the artefact, then file every output it produces as a record that names AIGE-OBL-CN-ALGOREC. Drawn from chapter 08.

Text alternative

Clause: China Algo. Rec., CAC Order No. 9.
Duty holder: Not stated.
Applies from: 2022-03-01, In force.
Artefact: Algorithm inventory with filing….
Layers: Layer 01, Layer 02, Layer 04.
Evidence record: Evidence record v1.
Record schema: Evidence record.
The same topic in 25 other frameworks; the crosswalk section below links each clause.

Id: AIGE-OBL-CN-ALGOREC
Instrument: China Provisions on Algorithmic Recommendation (2022) law
Clause: CAC Order No. 9
Applies from: 2022-03-01 In force · Binding; in force 2022-03-01

The artefact that evidences it

Algorithm inventory with filing record and number; security-assessment evidence pack; opt-out control at runtime.

Patterns that build it

No pattern in the catalogue names this clause on its "Maps to" line yet; the artefact above is the engineering answer.

The same topic in other frameworks

From the topic crosswalk: the clauses filed under the same topics as this one. Mappings are illustrative, not a claim of conformity.

Risk management

EU AI Act Art. 9 Risk management system (core)
ISO 42001 6.1.2 AI risk assessment (core)
ISO 42001 6.1.3 AI risk treatment (core)
ISO 42001 8.2 AI risk assessment (operation) (core)
ISO 42001 8.3 AI risk treatment (operation) (core)
NIST AI RMF MAP 1 MAP 1: Context is established and understood (core)
NIST AI RMF MAP 5 MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized (core)
NIST AI RMF MANAGE 1 MANAGE 1: AI risks based on assessments and other analytical output are prioritized, responded to, and managed (core)
TC260 Framework 3.0 TC260 2 Classification of AI safety risks (core)
TC260 Framework 3.0 TC260 Summary table Risks × technological × governance measures (core)
ISO 23894 23894 6.4 Risk assessment (core) (clause not verified)
ISO 23894 23894 6.5 Risk treatment (core) (clause not verified)
NIST AI RMF GOVERN 1.3 GOVERN 1.3: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization's risk tolerance (core)
NIST AI RMF MAP 1.5 MAP 1.5: Organizational risk tolerances are determined and documented (core)
NIST AI RMF MANAGE 1.3 MANAGE 1.3: Responses to the AI risks deemed high priority, as identified by the MAP function, are developed, planned, and documented (core)
NIST AI RMF MANAGE 1.4 MANAGE 1.4: Negative residual risks to both downstream acquirers of AI systems and end users are documented (core)
NIST AI RMF MEASURE 3 MEASURE 3: Mechanisms for tracking identified AI risks over time are in place (core)
CSA AICM GRC-02 Risk Management Program (core)
Korea AI Act Art. 34(1)(1) Risk management plan for high-impact AI (core)
UK ATRS ATRS 2.5.2 Risks and mitigations (core)
Singapore Agentic Agentic 2.1 Assess and bound the risks upfront (core)
CoE Convention CoE Art. 16 Risk and impact management framework (core)
prEN 18228 prEN 18228 AI risk management (draft; supports Art. 9) (core) (clause not verified)
GAO AI Accountability 1.6 Risk management: implement an AI-specific risk management plan to systematically identify, analyze, and mitigate risks (core)
ISO 42001 A.6 AI system life cycle
NIST AI RMF MEASURE 2 MEASURE 2: AI systems are evaluated for trustworthy characteristics
TC260 Framework 3.0 TC260 5.3.19 Re-assessment on material change
China GenAI Measures GenAI Art. 17 Security assessment and algorithm filing
EU AI Act Art. 3 Definitions
ISO 42001 6.1.4 AI system impact assessment
ISO 23894 23894 6.6 Monitoring and review (clause not verified)
GPAI Code Safety C1 Commitment 1: Safety and Security Framework
GPAI Code Safety C3 Commitment 3: Systemic risk analysis
CSA AICM MDS-12 Open Model Risk Assessment
OECD AI Principles OECD 1.5(c) Systematic risk management at each lifecycle phase

Governance and accountability

EU AI Act Art. 17 Quality management system (core)
ISO 42001 5.1 Leadership and commitment (core)
ISO 42001 5.2 AI policy (core)
ISO 42001 5.3 Roles, responsibilities and authorities (core)
ISO 42001 A.2 Policies related to AI (core)
ISO 42001 A.3 Internal organization (core)
NIST AI RMF GOVERN 1 GOVERN 1: Policies, processes, procedures, and practices across the organization related to the mapping, measuring, and managing of AI risks are in place, transparent, and implemented effectively (core)
NIST AI RMF GOVERN 2 GOVERN 2: Accountability structures are in place so that the appropriate teams and individuals are empowered, responsible, and trained (core)
TC260 Framework 3.0 TC260 4 Comprehensive governance measures (core)
TC260 Framework 3.0 TC260 5.3.12 Traceable chain of responsibility (core)
China GenAI Measures GenAI Art. 9 Provider responsibility as content producer (core)
China Deep Synthesis DeepSyn Art. 7 Information-security responsibility system (core)
GDPR Art. 5(2) Accountability (core)
ISO 42001 9.3 Management review (core) (clause not verified)
CSA AICM GRC-01 Governance Program Policy and Procedures (core)
CSA AICM GRC-06 Governance Responsibility Model (core)
UK ATRS ATRS 2.1 Owner and responsibility (core)
Singapore GenAI GenAI 1 Accountability (core)
Singapore Agentic Agentic 2.2.1 Clear allocation of responsibilities within and outside the organisation (core)
OECD AI Principles OECD 1.5 Accountability (core)
GAO AI Accountability 1.2 Roles and responsibilities: define clear roles, responsibilities, and delegation of authority for the AI system (core)
EU AI Act Art. 4 AI literacy
EU AI Act Art. 87 Reporting of infringements and protection of reporting persons
ISO 42001 7.2 Competence (clause not verified)
ISO 42001 9.2 Internal audit (clause not verified)
ISO 42001 10.1 Continual improvement (clause not verified)
NIST AI RMF GOVERN 4 GOVERN 4: Organizational teams are committed to a culture that considers and communicates AI risk
NIST AI RMF GOVERN 5 GOVERN 5: Processes are in place for robust engagement with relevant AI actors
GPAI Code Safety C8 Commitment 8: Systemic risk responsibility allocation
Korea AI Act Art. 36 Domestic representative
CoE Convention CoE Art. 9 Accountability and responsibility
G7 Code G7 Action 5 Develop, implement and disclose AI governance and risk-management policies
EN 18286 EN 18286 Quality management system for EU AI Act regulatory purposes
GAO AI Accountability 1.1 Clear goals: define clear goals and objectives for the AI system
GAO AI Accountability 1.3 Values: demonstrate a commitment to values and principles established by the entity

Documentation and transparency

EU AI Act Art. 11 Technical documentation (core)
EU AI Act Art. 13 Transparency and provision of information to deployers (core)
EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
ISO 42001 7.5 Documented information (core)
ISO 42001 A.6 AI system life cycle (core)
ISO 42001 A.8 Information for interested parties (core)
China AI Labelling Label Art. 4 Explicit labels for generated content (core)
China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
China GenAI Measures GenAI Art. 12 Labelling of generated content (core)
China Deep Synthesis DeepSyn Art. 16 Implicit technical labels (core)
China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (core)
GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
GDPR Arts. 13–14 Information to be provided to the data subject (core)
CSA AICM MDS-03 Model Documentation (core)
Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
UK ATRS ATRS Tier 1 Summary information (core)
Singapore GenAI GenAI 3 Trusted Development and Deployment (core)
CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities (core)
OECD AI Principles OECD 1.3 Transparency and explainability (core)
G7 Code G7 Action 3 Publicly report capabilities, limitations and domains of use (core)
GAO AI Accountability 1.9 Transparency: enable external stakeholders to access information on the design, operation, and limitations of the AI system (core)
GAO AI Accountability 3.5 Documentation: document the methods for assessment, performance metrics, and outcomes of the AI system (core)
EU AI Act Art. 50 Transparency obligations for providers and deployers of certain AI systems
NIST AI RMF MAP 1 MAP 1: Context is established and understood
NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability are examined and documented
China GenAI Measures GenAI Art. 19 Disclosure to regulators
GB/T 45654 GB/T 45654 Content labelling Generated-content labelling requirements (clause not verified)
EU AI Act Art. 86 Right to explanation of individual decision-making
EU AI Act Art. 18 Documentation keeping
EU AI Act Art. 43 Conformity assessment
EU AI Act Art. 53(1)(d) Public summary of the content used for training
EU AI Act Art. 50(2), 50(4) Machine-readable marking of synthetic content; disclosure of deep fakes
GPAI Code Transparency 1.2 Providing relevant information
GDPR Art. 30 Records of processing activities
NIST AI RMF MAP 1.6 MAP 1.6: System requirements are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks
NIST AI RMF MEASURE 2.9 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function to inform responsible use and governance
CSA AICM MDS-04 Model Documentation Requirements
Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview
UK ATRS ATRS 2.2 Description and rationale
CoE Convention CoE Art. 15(2) Notification of interaction with an AI system
GAO AI Accountability 1.7 Specifications: establish and document technical specifications

Inventory and registration

EU AI Act Art. 49 Registration (core)
EU AI Act Art. 71 EU database for high-risk AI systems (core)
ISO 42001 A.4 Resources for AI systems (core)
NIST AI RMF GOVERN 1.6 GOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities (core)
China Deep Synthesis DeepSyn Art. 19 Filing for public-opinion services (core)
China GenAI Measures GenAI Art. 17 Algorithm filing (core)
UK ATRS ATRS Tier 1 Summary information (the published record) (core)
EU AI Act Art. 6 Classification rules for high-risk AI systems
TC260 Framework 3.0 TC260 App. 2 II.2 Identity and access management
TC260 Framework 3.0 TC260 4.4.1 CII registration and filing
EU AI Act Art. 3(1) Definition of an AI system
EU AI Act Art. 52 Procedure
NIST AI RMF GOVERN 1.7 GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness
CSA AICM STA-08 Supply Chain Inventory
CSA AICM IAM-03 Identity Inventory
Korea AI Act Art. 33 Confirmation of high-impact AI
GAO AI Accountability 3.1 Documentation: catalog model and non-model components, along with operating specifications and parameters

Human oversight

EU AI Act Art. 14 Human oversight (core)
ISO 42001 A.9 Use of AI systems (core)
NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use (core)
TC260 Framework 3.0 TC260 App. 2 II.3 Strengthen human approval (core)
GDPR Art. 22 Automated individual decision-making, including profiling (core)
NIST AI RMF MAP 3.5 MAP 3.5: Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from the GOVERN function (core)
CSA AICM GRC-15 Human supervision (core)
Korea AI Act Art. 34(1)(4) Human management and supervision (core)
UK DUAA UK GDPR Art. 22C Safeguards for automated decision-making (core)
UK ATRS ATRS 2.3.2 Human review (core)
Singapore Agentic Agentic 2.2.2 Design for meaningful human oversight (core)
GAO AI Accountability 3.9 Human supervision: define and develop procedures for human supervision of the AI system (core)
EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
NIST AI RMF GOVERN 3.2 GOVERN 3.2: Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems
China GenAI Measures GenAI Art. 10 User guidance and protection
EU AI Act Art. 14(4)(b) Awareness of automation bias
OWASP Agentic ASI09 Human-Agent Trust Exploitation
CoE Convention CoE Art. 8 Transparency and oversight
OECD AI Principles OECD 1.2(b) Human agency and oversight safeguards

Runtime guardrails

ISO 42001 A.9 Use of AI systems (core)
NIST AI RMF MANAGE 2 MANAGE 2: Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by relevant AI actors (core)
TC260 Framework 3.0 TC260 App. 2 II.5 Dynamic runtime management (core)
TC260 Framework 3.0 TC260 3.2.1 Technological countermeasures for agentic AI (core)
China GenAI Measures GenAI Art. 10 Guided, bounded use (core)
China GenAI Measures GenAI Art. 14 Stop unlawful generation (core)
China Deep Synthesis DeepSyn Art. 10 Input and output review (core)
CSA AICM TVM-13 Guardrails (core)
CSA AICM AIS-09 Input Validation (core)
CSA AICM AIS-10 Output Validation (core)
OWASP LLM LLM01:2026 Prompt Injection (core)
OWASP LLM LLM10:2026 Improper Output Handling (core)
Singapore Agentic Agentic 2.3.1 During design and development, use technical controls (core)
EU AI Act Art. 5 Prohibited AI practices
EU AI Act Art. 15 Accuracy, robustness and cybersecurity
ISO 42001 A.6 AI system life cycle
EU AI Act Art. 5(1)(a)–(b) Manipulative techniques; exploitation of vulnerabilities
GPAI Code Safety C5 Commitment 5: Safety mitigations
OWASP LLM LLM06:2026 Unbounded Consumption

Incident response and monitoring

EU AI Act Art. 72 Post-market monitoring by providers and post-market monitoring plan (core)
EU AI Act Art. 73 Reporting of serious incidents (core)
ISO 42001 A.8 Information for interested parties (core)
ISO 42001 10.2 Nonconformity and corrective action (core)
NIST AI RMF MANAGE 4 MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored (core)
TC260 Framework 3.0 TC260 5.3.7 Real-time risk monitoring (core)
TC260 Framework 3.0 TC260 5.3.18 Incident reporting (core)
China GenAI Measures GenAI Art. 14 Handle and report unlawful content (core)
China GenAI Measures GenAI Art. 15 Complaint and reporting mechanism (core)
EU AI Act Art. 26(5) Deployer monitoring, informing the provider and suspending use (core)
GPAI Code Safety C9 Commitment 9: Serious incident reporting (core)
GDPR Arts. 33–34 Notification and communication of a personal data breach (core)
NIST AI RMF MANAGE 4.3 MANAGE 4.3: Incidents and errors are communicated to relevant AI actors, including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented (core)
CSA AICM SEF-07 Incident Management and Response (core)
CSA AICM SEF-08 Security Breach Notification (core)
Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold (core)
Singapore GenAI GenAI 4 Incident Reporting (core)
Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test (core)
G7 Code G7 Action 2 Identify and mitigate vulnerabilities, incidents and misuse after deployment (core)
G7 Code G7 Action 4 Responsible information sharing and reporting of incidents (core)
GAO AI Accountability 4.1 Planning: develop plans for continuous or routine monitoring of the AI system (core)
GAO AI Accountability 4.2 Drift: establish the range of data and model drift that is acceptable (core)
EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk
TC260 Framework 3.0 TC260 App. 2 II.6 Emergency plans
EU AI Act Art. 3(49) Definition of serious incident
EU AI Act Art. 20 Corrective actions and duty of information
GPAI Code Safety 3.5 Measure 3.5: Post-market monitoring
NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use
NIST AI RMF GOVERN 4.3 GOVERN 4.3: Organizational practices are in place to enable AI testing, identification of incidents, and information sharing

Fairness and non-discrimination

EU AI Act Art. 10(2)(f)–(g) Examination for possible biases; measures to detect, prevent and mitigate them (core)
EU AI Act Art. 4a Special-category data for bias detection (core)
GDPR Art. 5(1)(a) Lawfulness, fairness and transparency (core)
NIST AI RMF MEASURE 2.11 MEASURE 2.11: Fairness and bias as identified in the MAP function are evaluated and results are documented (core)
CSA AICM GRC-11 Bias and Fairness Assessment (core)
CoE Convention CoE Art. 10 Equality and non-discrimination (core)
China GenAI Measures GenAI Art. 4(2) Prevent discrimination in design, data, training and service (core)
GAO AI Accountability 2.7 Bias: assess reliability, quality, and representativeness of the data used in operation, including potential biases (core)
GAO AI Accountability 3.8 Bias: identify potential biases, inequities, and other societal concerns resulting from the AI system (core)
GDPR Art. 9 Processing of special categories of personal data
ISO 42001 A.5.4 Assessing AI system impact on individuals or groups of individuals (clause not verified)
NIST AI RMF GOVERN 3.1 GOVERN 3.1: Decision-making related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by a diverse team
UK ATRS ATRS 2.4.2 Model specification
OECD AI Principles OECD 1.2 Rule of law, human rights and democratic values, including fairness and privacy

Explainability and right to explanation

EU AI Act Art. 86 Right to explanation of individual decision-making (core)
EU AI Act Art. 13(3)(b)(iv)–(v) Information relevant to explain output; performance for specific persons or groups (core)
GDPR Art. 15(1)(h) Meaningful information about the logic involved (core)
NIST AI RMF MEASURE 2.9 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function to inform responsible use and governance (core)
CSA AICM GRC-13 Explainability Requirement (core)
CSA AICM GRC-14 Explainability Evaluation (core)
Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview (core)
UK DUAA UK GDPR Art. 22C Safeguards for automated decision-making (core)
OECD AI Principles OECD 1.3 Transparency and explainability (core)
GDPR Art. 13(2)(f) Existence of automated decision-making
GDPR Art. 22(3) Right to obtain human intervention and to contest the decision
ISO 42001 A.8.2 System documentation and information for users (clause not verified)
NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability as identified in the MAP function are examined and documented
UK ATRS ATRS 2.3.5 Appeals and review
Singapore GenAI GenAI 3 Trusted Development and Deployment
CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities

Source

Chapter 08, section China, checked against its sources on the review date above.

Machine-readable

This obligation: /api/v1/obligations/aige-obl-cn-algorec.json
The register: /api/v1/obligations.json · CSV
Schema and stability promise: open data and API

Cite this obligation

García Aibar, J. (2026). China Provisions on Algorithmic Recommendation (in force 2022-03-01) (AIGE-OBL-CN-ALGOREC). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-cn-algorec. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{China Provisions on Algorithmic Recommendation (in force 2022-03-01) (AIGE-OBL-CN-ALGOREC)}},
  howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
  year         = {2026},
  version      = {0.5.0},
  doi          = {10.5281/zenodo.22956197},
  url          = {https://aigovernanceengineer.com/obligations/aige-obl-cn-algorec},
  note         = {Version 0.5.0}
}