Lecture 5—The "Guiding State"; The Chinese Approach --for the Lecture Series: AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U.

 

Pix credit here (1960)

 

 I was delighted to have had the opportunity to present a series of Lectures hosted by the East China University of Political Science and Law (ECUPL) at the end of May 2026.

The overall theme (and thus the title) of the lectures was AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U, With a Sideways Glance at the U.N. The subject of the lectures requires little by way of introduction: Artificial intelligence is the broad term that has come to represent a growing cluster of non-human and digitalized processes and operations that has as its primary task the constitution of non-human systems capable of performing tasks that were once thought to require human intelligence. And so is the impulse to manage, control, exploit, embed, understand, and regulate these processes, systems, and perhaps eventually non-human consciousness with a huge potential to undertake many of the computational tasks (the mathematical and logical processing of data) that were once the sole domain of and perhaps defined what it meant to be human. That is the point where things get interesting. It is at the point where the development of machines, that is of non-human systems, capable of performing tasks that were once thought to require human intelligence, collide with regulatory structures meant to manage, contain, constrain, liberate, embed, project and exploit such non-human systems, whether they are traditional or emerging, public or private regulatory systems, that human collectives and the machine-systems they have created now find themselves.

The eight lectures progress sequentially from conceptual and theoretical frameworks (lectures 1 and 2, the objects and subjects of AI regulation), through a deeper consideration of regulatory systems in three distinguishable regulatory regimes--the US, EU, and China (Lectures 3, 4.5). The last two lectures consider judicial efforts to embed AI within traditional legal orders (Lecture 6), and the way in which the object of regulation (in the form of the owners of the larger AI enterprises) understand the relationship between AI, the state, and society (Lecture 7) . Lecture 8 summarizes and draws larger themes going forward.

In a previous post introducing Lecture 1 (From Algorithms to Foundation Models: What Contemporary AI is “Made of”) I suggested that perhaps a useful way of approaching the issue of AI regulation is to start by considering the nature and characteristics of the regulatory subject--what we euphemistically refer to as "AI." It then occurred to me that it might be useful as well to see if that regulatory object had views of their own respecting their nature character and, more importantly, the relationship of regulation projects to that (self) perception of their nature and character. So I approached Google's Gemini with a series of questions which I thought, in the process of what might pass for a conversation, might help humans begin to understand how at least one AI program thinks of itself. That conversation was incorporated into Lecture 1A. In Lecture 2 we moved from the object to the subjects of regurgitation. Like its regulatory objects, regulatory subjects  are functionally differentiated and can be disaggregated. In either case the connection between object and subject becomes complicated. 

This post includes a summary of the Lecture 5 Notes, as well as the link to the Lecture 5 PPT. Those interested may reach out to me to discuss availability of audio of the lecture and the full text of the Lecture 4 notes

Given the nature of the project I thought it might be useful to engage with an commercially available AI service for the production of a summary of the Lecture 1 materials. After some back and forth with  Perplexity (Lecture 4 used Grok; Lecture 3 used Anthropic's Claude; Lecture 2 used Chat GPT; Lecture 1 and 1A used Google's Gemini), we came up with the following abstract of Lecture 5. 

Gemini generated image from Perplexity instructions

ABSTRACT:  Lecture Five presents China’s approach to artificial intelligence not as an “integrated” regulatory system in the liberal-administrative sense, but as a coordinated governance formation embedded within the theoretical and institutional structures of Marxist-Leninist New Era thought. The materials situate AI within the organizing principles of Communist Party leadership, socialist modernization, and the continuous rationalization of state power. In this framing, AI is neither primarily a market product nor a discrete object of regulation; it is constituted as a strategic productive force and a modality of governance whose development must be guided along a socialist path.

The lecture draws on a set of legal and policy instruments—the New Generation Artificial Intelligence Development Plan (2017), the Cybersecurity Law, Data Security Law, Personal Information Protection Law, the Provisions on Algorithmic Recommendation, the Provisions on Deep Synthesis Internet Information Services, and the Interim Measures for Generative Artificial Intelligence Services—not as components of a unified regulatory code, but as elements of a coordinated architecture through which party-state priorities are operationalized across the AI stack. Coordination here reflects neither fragmentation nor integration in a Western sense, but alignment under the organizing principle of Party leadership, where law, policy, and technical systems function as mutually reinforcing modalities of governance.

Within this structure, the development-security dialectic expresses a core feature of New Era governance: modernization operates as both engine and vessel for the rationalization of the state. AI is advanced as a driver of industrial upgrading, technological capacity, and national rejuvenation, while simultaneously disciplined to ensure controllability, ideological alignment, and compatibility with social stability and national security. The question is not whether regulation constrains innovation, but how innovation can be produced within, and as an extension of, socialist governance.

The lecture emphasizes that governance attaches not only to AI systems as technical artifacts but to their effects within society, particularly through the lens of “public opinion attributes” and “social mobilization capacity.” The Provisions on Algorithmic Recommendation and the Deep Synthesis rules exemplify this approach: recommender systems and synthetic media are treated as infrastructures of perception and coordination, capable of shaping collective cognition, discourse, and action. Their governance is therefore inseparable from the Party-state’s responsibility for public opinion guidance and social order. Similarly, the Interim Measures on Generative AI position content-generating systems as public-facing instruments whose outputs must conform to legal, social, and ideological constraints, including the operationalization of Core Socialist Values.

Foundational statutes governing data—principally the Cybersecurity Law, Data Security Law, and Personal Information Protection Law—are situated within this same logic. Data is framed not only as an տնտեսական resource but as an object of sovereignty and a medium through which state rationality is exercised. Control over data flows, classification, and cross-border transfer becomes integral to maintaining both developmental capacity and systemic security.

Platforms occupy a critical role as coordinated governance actors. They are neither autonomous private entities nor mere regulatory subjects; rather, they function as intermediaries through which Party-state directives are translated into technical and operational practice. Algorithm design, content moderation, and system architecture become sites where political guidance is embedded within technological systems.

The lecture thus resists characterization of China’s AI governance as analogous to liberal regulatory models. Instead, it advances a conception of governance in which law, policy, ideology, and technology are coordinated under Party leadership to produce a form of digitally enabled socialist modernization. In comparative perspective, this distinguishes China from the United States’ market-centered model and the European Union’s rights-based supervisory framework. The Chinese approach is defined less by the calibration of regulatory intensity than by the subordination of AI development to a broader political project: the construction of a modern socialist state through coordinated, technologically mediated governance.

The essence is straightforward: Party-state leadership is the top-level principle; Socialist modernization is the governing purpose; The development-security dialectic is the core logic: advance AI, but keep it controllable; The main regulatory layers are data law, algorithmic recommendation, deep synthesis, and generative AI. These layers work through platforms as governance intermediaries. The outcome is AI shaped toward public opinion guidance, social stability, ideological alignment, and development under control.

 

 

Links to Lectures:

Lecture 0 -- Introduction
Lecture 1—From Algorithms to Foundation Models: What Contemporary AI is “Made of”
Lecture 1A--A Computation/Conversation With Google's "Maschinenmensch" Gemini:
Lecture 2—What Are We Actually Governing When We Govern AI?
Lecture 3—The “Markets State”: U.S. Approach
Lecture 4—The “Rights State”: EU Approach
Lecture 5—The “Guided State”: The Chinese Approach
Lecture 6—Courts, Companies, and the Legal Construction of AI
Lecture 7—AI Narratives: Palantir; Anthropic; Open AI; and Leopold Aschenbrenner
Lecture 8—Putting It All Together: Trends, Trend Lines, and Regulatory Dialectics

 

LECTURE NOTES 5 SUMMARY

 

Lecture Five introduces China’s approach to artificial intelligence governance as a distinct model that should not be understood through the familiar lenses of privacy regulation, product safety, or market oversight. Instead, students are asked to approach China’s system as a coordinated governance formation embedded in the broader political project of socialist modernization under Communist Party leadership. AI is framed not simply as a technology or a market product, but as strategic socio-technical infrastructure—both a productive force driving development and a governance tool through which the state rationalizes administration, social order, and ideological alignment.

The lecture situates China comparatively alongside the United States and the European Union. Recall that the United States emphasizes monitored market governance and innovation, while the European Union emphasizes risk-based supervision grounded in fundamental rights. China differs at the level of first principles. It constructs AI as part of a state-led modernization project in which development, security, and ideology are coordinated rather than separated. This means that governance is not primarily about limiting harm after the fact or classifying risk in advance, but about ensuring that AI systems evolve in ways that remain aligned with party-state priorities.

A key concept for students to understand is the development-security dialectic. Chinese policy consistently combines two imperatives: AI must advance economic growth, industrial upgrading, and technological capacity (development), while also remaining controllable, secure, and aligned with national security, social stability, and ideological requirements (security). These are not treated as competing goals in a liberal balancing sense. Instead, development is pursued through mechanisms that embed security, and security is designed to enable sustainable development under state supervision.

This dialectic is grounded in a broader theoretical framework associated with Marxist-Leninist New Era thought. Modernization is understood as both the engine and the vehicle for strengthening and rationalizing the state. AI plays a central role in this process. It is expected to transform industry, governance, and public services, while also enhancing the state’s capacity to manage society, including through data systems, predictive tools, and digital platforms. Students should therefore see AI not only as an economic technology but as part of a governance transformation.

Institutionally, China does not rely on a single comprehensive AI law. Instead, it uses a layered but coordinated set of legal and policy instruments. These include the New Generation Artificial Intelligence Development Plan (2017), the Cybersecurity Law, Data Security Law, Personal Information Protection Law, the Provisions on Algorithmic Recommendation, the Provisions on Deep Synthesis Internet Information Services, and the Interim Measures for Generative Artificial Intelligence Services. These instruments operate across different parts of the digital ecosystem—data, networks, algorithms, and content—but are aligned through common political objectives. Students should think of this as coordination under Party leadership rather than integration into a single legal code.

The foundation of this system is data governance. The Cybersecurity Law, Data Security Law, and Personal Information Protection Law regulate how data is collected, processed, classified, and transferred. Importantly, data is treated as both an economic resource and a matter of national security and sovereignty. This has direct implications for AI, since AI systems depend on large-scale data. Governance therefore focuses not only on privacy, but also on control over data flows, especially cross-border transfers, and on ensuring that data use aligns with national priorities.

Building on this foundation, the lecture highlights the importance of algorithmic recommendation regulation. The Provisions on Algorithmic Recommendation treat recommender systems as key infrastructures that shape attention, consumption, and public discourse. This is a major conceptual move: recommendation algorithms are not just technical tools but mechanisms of social influence. As a result, providers must implement governance systems, protect user data, avoid harmful or unlawful content, provide user controls, and in some cases file their algorithms with regulators. The trigger for heightened oversight is whether a system has “public opinion attributes” or “social mobilization capacity.”

Students should pay close attention to this concept. It captures the idea that AI systems are risky not only because they may be inaccurate or biased, but because they can shape how people think, what they see, and how they act collectively. This same logic appears in the regulation of deep synthesis technologies (such as deepfakes). The Deep Synthesis Provisions require labeling, identity verification, and content controls, reflecting concerns about misinformation, fraud, and broader disruptions to informational order and social trust.

The Interim Measures for Generative AI extend these principles to systems like chatbots and image generators. These measures promote the development of generative AI but impose obligations related to training data legality, intellectual property, personal information protection, and content governance. Outputs must comply with legal and political standards, including alignment with Core Socialist Values. Systems with significant influence over public opinion may be subject to additional review, including security assessments and algorithm filing.

At this point, students should recognize a pattern: governance attaches not only to the technology itself but to its social effects, especially its capacity to influence public opinion and social organization. This is a defining feature of the Chinese approach. It reflects a broader system of public opinion governance in which information flows are considered integral to political stability and state authority.

Another important element is the role of platforms. In China, large technology companies function as governance intermediaries. They are responsible for implementing regulatory requirements through system design, content moderation, data management, and compliance processes. This is not simply private self-regulation. It is a form of delegated governance in which platforms operationalize Party-state directives. Students should understand this as a key institutional mechanism: governance is distributed across actors but coordinated through political authority.

The lecture also emphasizes that China’s model includes strong support for AI development. Through industrial policy, the state promotes research, infrastructure, talent development, and domestic technological capacity. AI is central to economic modernization and geopolitical competition, particularly in light of external constraints such as export controls. This reinforces the development side of the development-security dialectic.

China’s global positioning on AI governance reflects similar themes. It emphasizes sovereignty, development rights, multilateral cooperation, and opposition to technological dominance by a few states. At the same time, its domestic system prioritizes control, alignment, and state authority. Students should understand this as a sovereignty-based approach: each state is seen as having the right to govern AI according to its own political system.

Finally, the lecture encourages students to think comparatively. The key question is not which system regulates AI more, but how each system defines AI and what it is for. In the United States, AI is primarily an innovation and market-driven technology. In the European Union, it is a risk-bearing system that must be aligned with rights and safety. In China, it is strategic infrastructure for socialist modernization and governance. These differences shape not only legal rules but also institutional design, enforcement, and the broader relationship between technology and society.

Students should leave the lecture with a clear understanding that China’s AI governance model is coordinated, state-centered, and developmentally oriented, with security and ideological alignment embedded throughout. The central analytical question is whether this model can sustain innovation while maintaining the levels of control and coherence that define its approach.