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| Image generated with Google Gemini |
Introduction:
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. My thanks to my
hosts and especially to Sun Yuhua for organizing these opportunities to engage
with colleagues.
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. AI has come to dominate , or infect, depending on one’s
point of view, virtually every aspect of the organization and operation of
collective human systems—as well as shaping the lives of individuals who are plugged
into virtual systems of decision making or automated processes that can provide
everything from entertainment, to advice, to interaction with other humans and organizational
structures. This is well known as is its
capacity for literal impact. One is reminded of Norbert Wiener’s reference to
the classic horror tale, the Monkey’s Paw, in his book God & Golem, Inc.: A Comment on Certain Points Where Cybernetics Impinges on Religion (MIT Press, 1964, pp. 58-60). The tale describes the risk of wishing for or
demanding something from a source that is entirely literal minded, which in the
case of the Monkey’s Paw was meant to grant three wishes, the means to those
ends were undefined. When a person wished for a sum of money, the wish was granted, in the
form of a settlement by their son’s employer as a consequence of an accident
that killed the son. In Wiener’s words: “The
magic of automation, and in particular the magic of automatization in which
devices learn, may be expected to be similarly literal. If you are playing a game according to certain rules and set the playing-machine to play for
victory, you will get victory if you get anything at all, and the machine will
not pay the slightest attention to any consideration except victory according
to the rules.” (Ibid.); unless, of course the rules are constantly changing to embed
more and more complex considerations. When the machine becomes self-learning,
it can develop its own mechanisms for adding or subtracting considerations as
well (rules, norms, values, etc.).
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. It is the point, as well, where such collectives are
floundering even as these machine-systems become ever more sophisticated and
ever more embedded in the life of human collectives and the engagement of
individual humans with the world. One moves here from "The Monkey's Paw" to "Three Thousand Years of Longing" where AI is the genie and his container is regulation, and the lonely professor given three wishes the consumers and producers of AI, that is the genie.
The point of this collision, especially among human
sub-systems, the cognitive orientation of which are becoming increasingly
better defined and distinct as against the others, that serves as the point of
departure for the series of lectures that together constitute AI Governance
in Comparative Perspective, Theory and Practice: China, U.S. and E.U, With a
Sideways Glance at the U.N. There are eight lectures:
Lecture 1—From Algorithms to Foundation Models: What Contemporary AI is “Made of”
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
The Lectures start from the premise that before it is even
possible to speak about regulation and regulatory systems, and even more so to
speak of such systems in or as some sort of comparison (against what standard
is yet another problem of course), one must first have firmly in mind two of
the critical elements on which any such discussion might be organized. The
first requires an effort to grasp, or
perhaps better to approach an understanding of, the object of regulation. The
second is to have some better sense of the regulatory enterprise, especially
its forms, placement, approaches, characteristics and logic; and noy just its
forms but its sources and the character of its authority within regulatory
collectives including but not limited to the governmental apparatus.
Once one understands the complexity and interconnectivity of
regulatory subjects (actors within a legal system, that might now also include
AI) and objects (the things that can be acted upon, including, perhaps the regulators
themselves), one can then approach the enterprise of regulation of AI in context.
This starting point is not merely
an
effort in taxonomy of regulatory subject and object, which then, as is
traditionally undertaken, serves as a basis for the sort of matrix within which
it is possible to consider those permutations and combinations of connections
between subject and object, thus disaggregated, that serves some purpose or
other. Each reflects the other in a certain sense.
AI is a creature made in the image of its
creator; the Biblical reference as well as reference to Mary Shelly’s
Frankenstein (creating something in the image of the creator) hides the
computational element in both, as well as their inter-subjectivity (here in the
sense of constant and mutual inter-penetration, sometimes as a form of
dialectic). But all of this interactivity, and its consequential impulse to build
AI in particular ways and to regulate it in equally peculiar ways, suggests an
underlying fundamental set of cognitive patterns that may be reflected in both
AI object and regulatory subject.
The regulatory object reflects its subject; it is its own subject. To regulate AI, then, suggests its character as the regulation of the self, now detached and reconstituted as a virtual representation of the self. Regulation is, in this sense, an effort in self control, but where the self has been detached (in this case the collective regulatory self) from and operates on the its physical representation (discussed from a conceptual perspective in 'The Soulful Machine, the Virtual Person, and the “Human” Condition').
Neural networks (pattern finding) and large language models
(predicting sequences) may provide a useful (or at least interesting) basis for
giving some form to that inter-subjectivity between regulatory subject and
object—that are also object and subject. Neural networks might provide a useful
conceptual basis for understanding , or at least framing, the iterative and
dialectical structures within which human collectives as well as computing
systems, are organized to process data (inputs, irritants, requests, etc.) through
layers of weights, values, biases and activation functions in order not just to
identify patterns but to use those patterns to rationalize data and respond to
stimulus. In the case of political economic systems, the neural network analogy
makes it easier to understand such systems as computing systems that identify
and reinforce patterns, and that rationalize the environment in which they operate,
through the identification and application of a series of foundational values,
biases, and recognized units for receiving, processing and passing along data
that together constitute the parameters within which it is possible to receive
process and identify objects received. For the purposes of these lectures
extracting the fundamental elements of the “hidden layers” of the political-economic
“neural network” of a state system is essential for understanding the way in
which the regulating body both understands the object of regulation and processes
that object in terms of the biases and values of the system to determine the
form and direction of regulation. This serves as the structure against which it might be possible to consider the (inevitable?) and distinctive regulatory pathways to identify and respond to "challenges" and "threats"-- take for example the challenge of AI "risk", that might be conceived as risk to rights in Europe, risk to markets in the US and risk to socialist modernization guided forward along its socialist path in China. Each of these reductionist expressions hides and embeds a much more potent (and effectively hidden) layer of analytics, premises, and ordering frameworks that make the reduction appear at some level "natural;" and so it is within the community of believers--
Fides et Ratio, perhaps more than
Magnifica humanitas (the former conceptual, the later operational).
For purposes of these lectures the focus is on three of the
more influential subjects of regulation: the United States (Lecture 3), the
European Union (Lecture 4), and China (Lecture 6). Mere description of regulatory
approaches is no longer particularly useful, since AI programs can now produce
descriptive summaries that may be better than anything a human can do
(controlling for the hallucinations of either). Each discussion of national
legislation starts with an effort to grasp the characteristics of each state’s
neural network embedded in and as its political economic model. Those characteristics then shape the approach
of each state toward the regulatory object and the character and objectives of
regulatory responses. In that context,
the United States might be labelled (or named; 鬼谷子 (Guiguzi's) ming-ming (明名 intelligent naming)) as the “markets state,” the European Union as the “rights
state,” and China as the “guiding state.” These labels suggest the baseline weights
and values, the scope of what each system identifies and processes, how it perceives
value and threat and accordingly regulates. The US is markets driven and
suspicious of state interference (for the most part though reshaped by
reconstituting efforts after the 1920s and its openness to techno bureaucratic
leadership). The European Union may be rights driven and trusting of state leadership
the purpose of which might be understood to protect and enhance rights and
prevent negative impacts. China is development driven with a specific objective
(forward movement along a Socialist Path) guided and led by a vanguard of
social forces in the context of which all productive forces of the state might
be embedded. These “hidden layers” of the national neural networks then shape
the way each formulates the “issue” of AI and fashions a response that both reaffirms
the patterning of its foundational structures and in the process reinforces
them.
Lectures 6 and 7 then consider two critical areas that have a significant
effect on any regulatory project. The first focuses on courts, enterprises, and
the legal construction of AI in and as law. Its principle focus is on the way
that AI related litigation is both changing traditional areas of jurisprudence
(tort, contract, property and the like) and is being changed by it. The second, Lecture 7, then considers the way in which leaders of AI production are seeking to
influence approaches to the conceptualization of the relationship between AI
and the state, and in the process offer a window into the way that AI leaders re-envision
the relationship between AI, the State and the regulation of both.To those ends the recent "concept exercises" or manifestos of Palantir, Anthropic, and Open AI, along with the reflections of an influential voice within the AI elite mainstream, Leopold Aschenbrenner, are considered.
Lecture 8 then attempts to draw the bigger picture. It
suggests the way that a shared vocabulary can cover quite substantial differences
in approach, values, and objectives in the context of AI and AI regulation. And
it suggests the way that weighting the objectives of innovation, risk, and the
role of the state affects the way in
which common regulatory focus of safety,
security, transparency, accountability, innovation, and infrastructure, each
with sometimes substantially different regulatory characteristics as a function
of the core differences in national “neural networks.” One comes at last to the understanding of the fundamental contradiction of AI and its regulation: the development of a common language across political systems the neural networks of which overlap but are fundamentally incompatible producing conceptions of AI and law that can be coordinated but which cannot converge. That seems to be the template for coordinated governance for the 2nd quarter of the 21st century. I noted the effect of patterns of conceptual homonyms in the context of the convergence project of the
International
Financial Reporting Standards (IFRS) Foundation has released its IFRS Foundation 2025
Annual Report—Fit for the Future (31 March 2026):.
By the start of the second quarter of the 21st century, however, the common language, like written Chinese, covered a wide variation in how it would be "spoken" and the signification with which common use terms would be infused. take transparency, for example. Within the cognitive cages of early 21st century convergence globalization transparency could be read broadly to require disclosure of virtually everything but trade secrets and protected know how--and even that might require some disclosure as a function, for example of its connection with adverse human rights or sustainability impacts (as these terms were to be developed by international institutions through hard and soft measures, norms, declarations, finds, etc.)--subject to increasingly narrowed exceptions for national security, development and the like. A quarter century later, transparency remains a presumption but now much more tightly constrained and perhaps sieved through superior obligations (many generated through domestic legal orders of States) respecting, national security, sanctions, data protection and data sovereignty, anti-espionage statutes, and blocking legislation to counter extraterritorial projections of regulatory systems. Transparency remains the same; its signification is now fractured and defined as a function of higher ranking signified principles, rules, and expectations. The same applies, with substantially more bite, to the construction and deployment of the concept of "risk."
One can then, and with enthusiasm, embrace a common language, while at the same time investing that language with regional or other contextual meaning, and constrain it through higher order local values, especially those encased in domestic (or regional) rules and legislation. The imaginaries of unity are preserved, and, indeed, a platform within which such values and contestations of values, may be produced and consumed, without enforcing a centralizing fundamental political or normative line. Yet that also is a step forward. A single language encasing difference may not have been the goal at the start of the 21st century, but language binds all the same--it binds enough that difference may be constrained by the outer boundaries of rational learning inside a cognitive cage that through its language objects effectively defines both that rationality and its limits. And it is that common language that provides all that is necessary to achieve the fundamentally critical element of this project: comparability and communication across and despite of difference. The rest might come later; or it might not. That convergence is no longer necessary where a common language has been substituted for common norms, beliefs, practices, and expectations. (A
Common Language Containing Differentiating Meanings Within Evolving
International Standards for Sustainability Disclosure in Financial
Statements).
In the posts that follow I will post summaries of the lecture notes and the PowerPoint of each of the lectures which can be accessed by following these links:
Lecture 1—From Algorithms to Foundation Models: What Contemporary AI is “Made of”
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
