Current global AI governance discussions mostly focus on regulations and industry supervision, often overlooking underlying cognitive safety and model-native technical risks. The Carbon-Silicon Tradition has released the 'AI Safety and Civilization Governance Codex', a complete set of 188 episodes, building a seven-layer progressive research framework. From ordinary people's AI cognitive risks, to the underlying safety mechanisms of large models, then to industry rights and responsibilities and national computing-power supervision, and ultimately extending to long-term risk control at the scale of cross-generational civilization. This system attempts to connect technical safety, legal rights and responsibilities, industry implementation, and civilization governance, providing a complete reference paradigm for AI safety and carbon-silicon symbiotic development.
Layer One: Foundational Cognitive Safety Layer (001–024)
Aimed at building basic AI risk discrimination capabilities for the public and practitioners, distinguishing ordinary information errors from cognitive pollution, and dismantling the AGI market bubble and capability illusion; it is the foundational safety base of the entire codex.
001|Layered classification system of AI risks and objective assessment baselines
002|Analysis of the core boundary between specialized AI and artificial general intelligence
003|Identification of the causes of AI capability illusion and judgment benchmarks
004|Formation types and risk boundaries of AI algorithmic bias
005|Tracing AGI market narrative bubbles and public cognitive biases
006|The essential difference between cognitive pollution and ordinary information errors
007|Blind spots and limitations of contemporary AI safety research paradigms
008|The safety value and inherent shortcomings of AI red-team testing
009|Differences in judging AI demo capabilities versus real deployment capabilities
010|Formation mechanisms and safety risks of human-machine cognitive dependence
011|Risk characteristics and evolution patterns of the weak AI stage
012|Cognitive amplification effects in the dissemination of AI content
013|A short-, medium-, and long-term temporal layering framework for AI risks
014|Observation boundaries of AI capability emergence and correcting overinterpretation
015|A simple method for ordinary users to assess AI output risks
016|Pattern recognition of exaggerated media AI publicity narratives
017|Cognitive solidification risks from AI recommendation systems combined with information cocoons
018|The accurate definition of AI safety: controllable risk rather than zero risk
019|Comparison of basic safety features of open-source and closed-source AI models
020|Reference boundaries of AI-assisted decision-making and human final-review authority
021|Risk differences between AI false content and traditional internet rumors
022|Distortion problems caused by sample bias in AI risk assessment
023|Counterexample calibration for scenarios where basic safety rules do not apply
024|Summary of global assessment baselines for the foundational cognitive safety layer
Layer Two: Technical Risk Boundary Layer (025–054)
Descending into the underlying technical mechanisms of large models, covering native technical safety defects such as model black boxes, prompt injection, data poisoning, backdoors, adversarial samples, and multi-model interconnection; all governance rules are built upon the technical feasibility of this layer.
025 | Security governance challenges arising from the black-box nature of large models
026 | Underlying mechanisms of AI model alignment failure and reward tampering
027 | Cognitive entropy increase: semantic drift patterns in large-scale AI distribution
028 | Principles of prompt injection attacks and baseline protection standards
029 | Types of training data poisoning attacks and detection shortcomings
030 | Concealment mechanisms and security risks of model weight backdoors
031 | Formation conditions of model jailbreak behavior and management boundaries
032 | Analysis of model loss-of-control risks triggered by out-of-distribution inputs
033 | Risks of training data memorization and privacy leakage in large models
034 | Capability boundaries and existing defects of explainable AI technology
035 | Security drift and version control in model iterative updates
036 | Identification and risk-control challenges of implicit harmful model outputs
037 | Protection limitations and bypass mechanisms of AI safety guardrails
038 | Risk governance of resource-exhaustion attacks on large model inference
039 | Cross-media covert poisoning risks of multimodal models
040 | Risk management of unpredictable emergent model capabilities
041 | Static security audit processes for AI models and their applicable boundaries
042 | Advantages and testing blind spots of dynamic red-team auditing
03 | 043 | Security degradation caused by model quantization, distillation and lightweighting
044 | Mechanisms of cross-model risk transfer caused by transfer learning
045 | Security isolation standards for large model API deployment modes
046 | Security rules for model weight lifecycle destruction and retention
047 | Risks of accelerated cognitive entropy diffusion in multi-model interconnection scenarios
048 | Defects and distortion remediation of AI safety evaluation metrics
049 | Principles of adversarial sample attacks and constraints of protection boundaries
050 | Risk management of autonomous chained behaviors by AI agents
051 | Construction standards and shortcomings of AI safety test datasets
052 | Counterexample calibration for scenarios where technical safety rules do not apply
053 | Summary of global audit baselines for the technical risk boundary layer
054 | Global lock-in of the core terminology system for model safety
Layer 3: Human-Machine Rights and Responsibility Attribution Layer (055–081)
Focused on the division of responsibility after risks occur. Clarifying copyright of human-machine generated works, content provenance, and multi-party accountability mechanisms, transforming AI safety risks into implementable legal, academic, and platform rights-and-responsibility standards.
055 | Basic Framework of the Three-Part Rights Attribution System for Human-Machine Generated Content
056 | The Judicial Determination Logic in Copyright Disputes over AI-Generated Content
057|The Boundaries of Training Data Copyright Use and the Bottom Line of Compliance
058|The Capability Boundaries and Applicable Scenarios of AI Content Provenance Technology
059|Deepfake Content Risks and Standards for Dissemination Control
060 | Allocating Multi-Party Responsibility for Harm Caused by AI Misinformation
061| Ownership of Open-Source Model Weights and the Boundaries of Commercial Use Rights
062|Legal Boundaries of the Right to Use and Ownership of Closed-Source Large Models
063|Rules on AI-Assisted Research Attribution and Academic Norms
064 | Criteria for Determining the Ownership of AI Literary and Artistic Creation Output
065 | Accountability Mechanisms for AI-Generated Fake Academic Content
066|A Quantitative Method for Determining Contribution in Human-Machine Collaborative Creation
067|Specification for AI Content Tampering Detection and Evidence Preservation
068|The Boundary of Review Responsibilities of Platforms Distributing AI-Generated Content
069 | Special Protection Rules for AI-Generated Content Involving Minors
070|Norms for Defining Rights and Responsibilities of AI-Generated Government Affairs Content
071|Conflicts over the ownership and accountability of cross-border AI-generated content
072 | Determining the Copyright Chain of AI-Iterated Derivative Works
073|Defining the Asset Attributes of AI-Generated Data
074|Liability Standards for Negligence by Users of AI Tools
075|Implicit Responsibilities of Model Developers and the Boundaries of Risk Backstopping
076|Platform Immunity Exceptions for AI Content Misjudgment and Erroneous Review
077|AI Rights Attribution Dispute Evidence Collection Process and Evidence Standards
078|Dynamic Adaptation Mechanism for AI Rights Confirmation
079|Calibration of Counterexamples for Scenarios Where Ownership Confirmation Rules Do Not Apply
080 | Global Standards Summary for the Human-Machine Accountability Attribution Layer
081|Global Standardization of Core Terminology in the Rights Confirmation System
Layer 4: Industry-wide Deployment and Governance Layer (082–108)
This pushes security and rights-confirmation rules down into vertical industry scenarios. It covers healthcare, education, scientific research, finance, media, law, industry, government affairs, and other fields, tailoring security boundaries, human final-review mechanisms, and risk thresholds for each industry.
082|Safety Boundaries and Responsibility Norms for Medical AI-Assisted Diagnosis
083|Data Privacy Protection and De-identification Norms for Medical AI
084|Cognitive Guidance Risks and Youth Protection Rules for Educational AI
085|Governance Specification for Fairness in Educational AI Content Delivery
086|Risk Control Standard for Authenticity of Research AI Experiment Simulation
087|Academic Compliance Boundaries for Research AI-Assisted Paper Writing
088|Fairness of Financial AI Risk Control Models and Anti-Discrimination in Algorithms
089|Rights, Responsibilities, and Risk Boundaries of Financial AI Credit Assessment
090|Governance Specification for Authenticity of Media AI News Generation
091|Risks and Content Control in Short-Video AI Content Ecosystems
092|Validity Boundary Specification for Legal AI-Assisted Document Generation
093|Liability Allocation and Risk Warnings for Legal Consultation AI Output
094|Security Isolation Specification for Industrial Large Model Production Line Integration
095|Fault Tolerance and Human Final Review Mechanisms for Intelligent Manufacturing AI Decision-Making
096|Fairness and Risk Control Baseline for Government AI Public Decision-Making
097|Algorithm Transparency and Protection of Public Rights in Urban Governance AI
098|Compliance of E-Commerce AI Recommendation Algorithms and Consumer Rights Boundaries
099|Anti-Discrimination in Recruitment AI Algorithms and Fair Employment Governance
100|Safety Redundancy and Risk Control in Transportation AI Dispatch Systems
101|Governance Specification for Authenticity of Cultural Tourism AI Content Creation
102|Security Isolation Principles for Military-Civilian Cross-Domain AI Scenarios
103|Safety and Data Specifications for Intelligent Agricultural AI Applications
104|Risk Warnings and Liability Boundaries for Meteorological AI Prediction Output
105|General Risk Stratification and Adaptation Rules for Industry AI
106|Counter-Example Calibration for Exception Scenarios in Industry Governance Rules
107|Summary of Unified Baselines for Cross-Industry AI Governance
108|Global Alignment of Core Industry Governance Terminology
Layer Five: Top-Level Regulatory Layer for Algorithms and Computing Power (109–135)
Ascending to the dimension of industry and national top-level regulation. This covers research on computing power oligopolies, model access, tiered regulation, cross-border model flows, security audits, and incident emergency response, controlling AI computing power and model safety from the supply side.
109|Market Access Mechanisms and Security Filing Specifications for Large Models
110|Governance of Oligopoly Risks from AI Computing Resource Concentration
111|Fairness of Computing Power Allocation and Public Resource Regulatory Baselines
112|Tiered Regulatory System for High-, Medium-, and Low-Risk AI Models
113|Continuous Dynamic Assessment and Annual Inspection Specifications for AI Models
114|Upstream and Downstream Liability Transmission Mechanisms for Model Distribution Platforms
115 | AI Open-Source Community Governance Norms and Risk Constraints
116 | Transparency Baseline for Closed-Source Commercial Model Regulation
117 | Security Review Norms for Cross-Border AI Model Flows
118 | Risk Control for Cross-Border Computing Power Scheduling and Data Export
119 | Compliance Governance and Traffic Constraints for AI Algorithm Recommendation
120 | Regulatory Rules for Algorithmic Black-Box Commercial Abuse Risks
121 | Complementary Mechanism of AI Industry Self-Discipline and Mandatory Regulation
122 | Qualification and Review Standards for AI Security Audit Institutions
123 | Regulatory Implementation Process for AI Incident Accountability
124 | Boundaries of Public Disclosure and De-Identification of Public-Domain AI Algorithms
125 | Access Security Standards for AI Systems in Government Procurement
126 | Dynamic Mechanism for AI Industrial Policy to Adapt to Technological Iteration
127 | Normalized Risk Control for Attack-Defense Risks in Computing Infrastructure
128 | Regulation of Large Model Version Updates and Change Filing Rules
129 | AI Emergency Response Mechanisms and Risk Disposal Processes
130 | Identification Standards for Algorithmic Monopoly and the Antitrust Governance Baseline
131 | Norms for the Regulatory Boundaries of AI Data Element Circulation
132 | Grayscale Mechanism for Regulatory Rules to Adapt to Technological Iteration
133 | Counterexample Calibration for Top-Level Regulation in Exception Scenarios
134 | Summary of the Global Baseline for Top-Level Regulation of Algorithms and Computing Power
135 | Global Lock-In of Core Terminology in the Top-Level Regulatory System
Level 6: Civilization-Level Cognitive Risk Control Layer (136–162)
Stepping beyond the scope of law and industry, this level takes the long-cycle perspective of civilization. It studies long-term slow-variable security risks such as group cognitive drift, cognitive class stratification brought about by computing power, cross-generational cognitive contamination, and conflicts of values in global governance.
136 | Organizational Cognitive Self-Lock Phenomena in the AI Era and Breakthrough Mechanisms
137 | Risks of Paradigm Rigidity in Industry Knowledge and Civilization-Level Risk Control
138 | Risk of Group Judgment Baseline Drift Caused by Long-Term AI Use
139 | Cognitive Class Stratification: Civilizational Risks of Information and Computing Asymmetry
140 | Mismatch Between AI Technology Iteration and the Speed of Social Institutional Iteration
141 | The Tragedy of the Commons in AI Security Investment and Industry Collaborative Governance
142 | Differences in Global AI Governance Values and Conflict Coordination
143 | Transnational AI Cognitive Penetration and the Security Baseline of Public Narratives
144 | Risks of Disciplinary Generational Gaps from AI Reshaping Knowledge Systems
145|Long-term capability degradation risks of AI cognitive dependence among youth groups
146|Compression risks of AI standardized narratives on pluralistic thought
147|A civilization-level judgment framework for the ethical boundaries of artificial intelligence
148|Formation mechanisms of AI technological hegemony and counterbalancing rules
149|Civilization-level baseline norms for preserving human judgment authority
150|Long-term civilizational hazards of cross-generational accumulation of AI cognitive pollution
151|Patterns of civilizational order reshaping in the evolution of general intelligence
152|Research on differentiated AI governance paths across multiple civilizational systems
153|AI public opinion ecosystem stability and baseline for cognitive security governance
154|Technological governance paths for rebuilding public reason in the AI era
155|Society-level risks of emergent group behavior among intelligent agents
156|Research on boundary thresholds for AI decision-making replacing human decision-making
157|Counterexample calibration for exception scenarios in civilization-level risk control systems
158|Path for building a unified global consensus on AI safety governance
159|Temporal deduction framework for long-term AI civilizational risks
160|Finalization of the core model for civilization-level cognitive risk control
161|Summary of global governance baselines at the civilizational risk control layer
162|Global terminology alignment lock for the civilizational risk control system
Seventh Layer: General AI Codex Terminal Layer (163–188)
Consolidating all conclusions of the previous six layers, establishing an axiom system for a carbon-silicon symbiotic civilization. It distinguishes three differentiated governance rule sets for weak AI, early AGI, and mature AGI, defines codex revision and academic/policy/media citation norms, and completes the closed loop of the entire system.
163|Archival foundation of the carbon-silicon orthodox civilization baseline axiom system
164|Mapping of the global logical relationships among the 188 codex provisions
165|Complete adaptation norms for the weak AI stage governance baseline
166|Governance rule switching mechanism for the early AGI transition stage
167|Construction of a civilization-level governance stability framework for mature AGI
168|Tiered applicability and scenario matching mechanism for codex provisions
169|Codex version iteration and official revision trigger standards
170|Codex review mechanism and industry consensus update process
171|Adaptation guide for judicial implementation of AI safety and governance
172|Baseline of the industry implementation execution manual for AI safety and governance
173|Adaptation norms for public policy citation of codex standards
174|Standard paradigm for academic research citation of the codex system
175|Communication Norms of the Media Public Narrative Citation Codex
176|Statement on the Applicable Boundaries and Non-Coverage Scenarios of the Codex System
177|Existing Limitations of the Codex System and Future Iteration Directions
178|Underlying Constraint Rules for the Carbon-Silicon Symbiotic Human-Machine Civilization Order
179|The Ultimate Governance Baseline for the Steady-State Operation of AI Civilization
180|Closed-Loop Review of Global Risks in AI Safety and Civilization Governance
181|General Cross-Level Logical Closed-Loop Verification of the Seven-Layer System
182|Overall Adaptation Plan for Industry Implementation of the Entire Codex System
183|Overall Adaptation Baseline for National Governance of the Entire Codex System
184|General Framework of Civilization-Level Value Order for the Entire Codex System
185|Archived Governance Contingency Plan System for AGI Bubble Cycles
186|Final Verification of the Entire System Against Loopholes, Shortcomings and Blind Spots
187|Announcement of the Final Version of the AI Safety and Civilization Governance Codex
188|Permanent Lock and Archive Declaration of the Carbon-Silicon Orthodox AI Civilization Governance Baseline