Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 586–588, that near-term and catastrophic AI risks are complementary rather than zero-sum. Many institutional measures can address both, and recognition of each supplies substantive and political support for the other. This is significant because factional conflict over time horizons can obstruct a coalition with overlapping regulatory interests. It connects to near-term harms, existential risk, coalition politics, common ground, AI regulation, false choice.

near-term and catastrophic AI risks are complementary rather than zero-sum. This matters because factional conflict over time horizons can obstruct a coalition with overlapping regulatory interests.

near-term harms
existential risk
coalition politics
common ground
AI regulation
false choice

The authors do not erase genuine disagreements about priority, evidence, or particular interventions.

Pages 586-588 critique the polarized public debate and state the complementary-risk thesis.


Yonathan A. Arbel, Matthew Tokson, and Albert Lin

Yonathan A. Arbel, Matthew Tokson & Albert Lin, Systemic Regulation of Artificial Intelligence, 56 Ariz. St. L.J. 545 (2024).

Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 587–588, that regulation of immediate harms can create infrastructure for responding to more dangerous future systems. Existing review laws can be amended, litigation can surface warning signs, and pre-screening can identify risks before deployment. This is significant because institutional capacity built for present accountability has option value under future uncertainty. It connects to institution building, legal adaptation, pre-screening, litigation, future risk, regulatory capacity.

regulation of immediate harms can create infrastructure for responding to more dangerous future systems. This matters because institutional capacity built for present accountability has option value under future uncertainty.

institution building
legal adaptation
pre-screening
litigation
future risk
regulatory capacity

Early institutions may require major revision if future systems differ radically.

Pages 587-588 identify concrete pathways from present-harm rules to future governance.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 588, that acknowledging catastrophic risk can strengthen regulation of current discrimination, privacy, and fraud. It can resolve ambiguity in the regulatory calculus and mobilize people who are not otherwise focused on distributive harms. This is significant because a broader coalition can support comprehensive institutions that protect against multiple types of injury. It connects to political coalition, catastrophic risk, civil rights, privacy, fraud, regulatory momentum.

acknowledging catastrophic risk can strengthen regulation of current discrimination, privacy, and fraud. This matters because a broader coalition can support comprehensive institutions that protect against multiple types of injury.

political coalition
catastrophic risk
civil rights
privacy
fraud
regulatory momentum

Coalition breadth does not guarantee agreement on policy design and can create strategic tensions.

Page 588 explains the practical and political contribution of long-term risk recognition.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 588, that systemic oversight does not displace targeted AI laws. Legislatures can separately address discrimination, fraud, recursive improvement, or autonomous weapons while also governing shared infrastructure. This is significant because layered regulation can match different mechanisms and severities of harm. It connects to targeted regulation, systemic oversight, discrimination law, autonomous weapons, regulatory layering, legislatures.

systemic oversight does not displace targeted AI laws. This matters because layered regulation can match different mechanisms and severities of harm.

targeted regulation
systemic oversight
discrimination law
autonomous weapons
regulatory layering
legislatures

The article does not specify preemption rules or resolve overlaps among regimes.

Page 588 expressly preserves a role for particularized legislation.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 589–590, that AI is both unusually uncertain and capable of broad, transformative benefits. Potential gains include agriculture, environmental monitoring, medicine, information access, education, energy, human-rights monitoring, and disaster response. This is significant because regulatory analysis must count foregone benefits rather than treating risk reduction as costless. It connects to cost-benefit analysis, AI for social good, medicine, education, environment, disaster response.

AI is both unusually uncertain and capable of broad, transformative benefits. This matters because regulatory analysis must count foregone benefits rather than treating risk reduction as costless.

cost-benefit analysis
AI for social good
medicine
education
environment
disaster response

The list is illustrative and includes projected as well as demonstrated benefits.

Pages 589-590 place a wide benefit inventory alongside the technology's novel features.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 590, that the regulatory aim is not to ban AI development but to identify justified interventions relative to a largely unregulated baseline. A broad margin exists for safeguards even while research and socially valuable deployment continue. This is significant because debate should compare feasible regimes rather than regulation with technological extinction. It connects to regulatory baseline, marginal analysis, innovation policy, AI development, risk mitigation, proportionality.

the regulatory aim is not to ban AI development but to identify justified interventions relative to a largely unregulated baseline. This matters because debate should compare feasible regimes rather than regulation with technological extinction.

regulatory baseline
marginal analysis
innovation policy
AI development
risk mitigation
proportionality

The article does not calculate the optimal stringency of every intervention.

Page 590 expressly disclaims a ban and frames the comparison at the margin.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 590–591, that many celebrated AI benefits are inseparable from serious downside mechanisms. Growth can displace labor, prediction can reproduce discrimination and invade privacy, surveillance can trade liberty for security, and autonomous weapons can lower the cost of war. This is significant because benefit labels cannot replace analysis of who pays and which rights are compromised by the same capability. It connects to benefit-risk coupling, labor displacement, privacy, surveillance, autonomous weapons, distribution.

many celebrated AI benefits are inseparable from serious downside mechanisms. This matters because benefit labels cannot replace analysis of who pays and which rights are compromised by the same capability.

benefit-risk coupling
labor displacement
privacy
surveillance
autonomous weapons
distribution

Coupling does not mean the downsides cannot be mitigated or that every use has a negative balance.

Pages 590-591 pair specific AI benefits with their corresponding risks.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 591, that AI belongs with beneficial but dangerous technologies that society regulates rather than abandons. Coal, nuclear power, and pathogen research all produce value while requiring controls against serious external harm. This is significant because the existence of large benefits is compatible with licensing, safety rules, and restricted access. It connects to dangerous technologies, nuclear regulation, biosecurity, externalities, licensing, comparative regulation.

AI belongs with beneficial but dangerous technologies that society regulates rather than abandons. This matters because the existence of large benefits is compatible with licensing, safety rules, and restricted access.

dangerous technologies
nuclear regulation
biosecurity
externalities
licensing
comparative regulation

The analogies differ from AI in detectability, physical footprint, and institutional maturity.

Page 591 uses three regulated technologies to rebut the claim that benefit defeats regulation.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 592, that uncertainty applies to AI's hoped-for benefits as much as to its feared harms. A skeptic cannot discount risk merely because the future is hard to predict while treating gains as assured. This is significant because symmetrical treatment of uncertainty prevents an unreasoned presumption for acceleration. It connects to uncertainty, cost-benefit analysis, burden of proof, AI optimism, risk skepticism, epistemic consistency.

uncertainty applies to AI's hoped-for benefits as much as to its feared harms. This matters because symmetrical treatment of uncertainty prevents an unreasoned presumption for acceleration.

uncertainty
cost-benefit analysis
burden of proof
AI optimism
risk skepticism
epistemic consistency

The argument does not establish that probabilities or magnitudes on both sides are equal.

Page 592 identifies irreducible uncertainty on both sides of the regulatory ledger.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 592, that the precautionary principle is strongest when probabilities are unclear and the downside may be catastrophic. The article adopts an anti-catastrophe version of precaution rather than a rule against every uncertain activity. This is significant because special treatment of irreversible tail risks can be compatible with ordinary cost-benefit reasoning elsewhere. It connects to precautionary principle, anti-catastrophe principle, tail risk, irreversibility, climate analogy, risk regulation.

the precautionary principle is strongest when probabilities are unclear and the downside may be catastrophic. This matters because special treatment of irreversible tail risks can be compatible with ordinary cost-benefit reasoning elsewhere.

precautionary principle
anti-catastrophe principle
tail risk
irreversibility
climate analogy
risk regulation

Precaution must be bounded because regulation itself can generate costs and risks.

Page 592 invokes Sunstein, climate risk, and Posner to define the relevant form of precaution.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 592–593, that maximin counsels choosing the policy with the best plausible worst-case outcome under deep uncertainty. Regulators should not wait decades for probabilities to become precise when a preventable catastrophe is at stake. This is significant because worst-case protection may maximize welfare where losses are extreme and uncertainty is irreducible. It connects to maximin, decision theory, worst-case analysis, deep uncertainty, catastrophic harm, preventive regulation.

maximin counsels choosing the policy with the best plausible worst-case outcome under deep uncertainty. This matters because worst-case protection may maximize welfare where losses are extreme and uncertainty is irreducible.

maximin
decision theory
worst-case analysis
deep uncertainty
catastrophic harm
preventive regulation

Maximin is controversial and can be overinclusive if worst cases are unconstrained or implausible.

Pages 592-593 define maximin and apply it to AI.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 593, that precautionary AI regulation need not suppress every deployment. Many plausible benefits themselves carry harms, and a regulatory regime can differentiate uses rather than impose a complete ban. This is significant because the strongest accelerationist objection attacks a caricature if governance can preserve lower-risk innovation. It connects to proportional regulation, innovation, AI deployment, risk tiers, accelerationism, policy design.

precautionary AI regulation need not suppress every deployment. This matters because the strongest accelerationist objection attacks a caricature if governance can preserve lower-risk innovation.

proportional regulation
innovation
AI deployment
risk tiers
accelerationism
policy design

The article does not supply a quantitative threshold for permissible risk.

Page 593 gives the first answer to objections against maximin regulation.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 593, that avoiding extinction may be justified even at the cost of extraordinarily large foregone gains. Existing human life and continued human innovation have immense value independent of speculative superintelligent benefits. This is significant because a small chance of irreversible loss can dominate expected gains when the thing at risk is humanity's continued existence. It connects to existential risk, foregone benefits, human value, innovation, precaution, intergenerational ethics.

avoiding extinction may be justified even at the cost of extraordinarily large foregone gains. This matters because a small chance of irreversible loss can dominate expected gains when the thing at risk is humanity's continued existence.

existential risk
foregone benefits
human value
innovation
precaution
intergenerational ethics

The argument depends on treating the extinction pathway as plausible and nontrivial.

Page 593 gives the second answer to accelerationist objections.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 593–594, that AI rules can be relaxed temporarily if advanced systems become necessary to answer an external emergency. A future asteroid or pandemic could justify scaling down restrictions while the threat persists. This is significant because regulatory flexibility weakens the claim that present caution permanently sacrifices every future protective use. It connects to emergency powers, regulatory flexibility, asteroid defense, pandemic response, adaptive governance, AI benefits.

AI rules can be relaxed temporarily if advanced systems become necessary to answer an external emergency. This matters because regulatory flexibility weakens the claim that present caution permanently sacrifices every future protective use.

emergency powers
regulatory flexibility
asteroid defense
pandemic response
adaptive governance
AI benefits

The authors reject using uncertain future emergencies to justify an unregulated baseline today.

Pages 593-594 give the third answer and qualify the emergency exception.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 594, that present generations have a prima facie moral duty to err toward preserving humanity. Future generations can later accelerate if knowledge improves, but extinction would eliminate their ability to choose. This is significant because reversibility and intergenerational agency support caution under current ignorance. It connects to intergenerational justice, reversibility, human dignity, moral duty, future generations, AI governance.

present generations have a prima facie moral duty to err toward preserving humanity. This matters because reversibility and intergenerational agency support caution under current ignorance.

intergenerational justice
reversibility
human dignity
moral duty
future generations
AI governance

The duty is described as prima facie and does not mechanically decide every policy tradeoff.

Page 594 states the ethical and pragmatic case for the safer route.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 594–595, that human extinction would erase not only lives but the continuing narrative that gives present contributions lasting significance. The article treats humanity's future as a moral object beyond the sum of individual deaths. This is significant because existential risk implicates meaning, inheritance, and unrealized human flourishing as well as mortality. It connects to extinction, human meaning, future generations, existential ethics, collective value, catastrophic harm.

human extinction would erase not only lives but the continuing narrative that gives present contributions lasting significance. This matters because existential risk implicates meaning, inheritance, and unrealized human flourishing as well as mortality.

extinction
human meaning
future generations
existential ethics
collective value
catastrophic harm

The argument is philosophical and does not purport to settle all debates about population ethics.

Pages 594-595 describe the scale and qualitative character of extinction loss.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 595, that domestic AI policy should begin from seven general principles rather than wait for a fully specified comprehensive code. The principles identify regulatory level, timing, international fit, safety research, tool diversity, high-risk pathways, and state participation. This is significant because a flexible architecture can guide near-term lawmaking while technical and geopolitical details continue to change. It connects to regulatory principles, domestic policy, adaptive governance, AI legislation, institutional design, policy roadmap.

domestic AI policy should begin from seven general principles rather than wait for a fully specified comprehensive code. This matters because a flexible architecture can guide near-term lawmaking while technical and geopolitical details continue to change.

regulatory principles
domestic policy
adaptive governance
AI legislation
institutional design
policy roadmap

The principles are intentionally programmatic and require implementation details.

Page 595 introduces the domestic program and its level of abstraction.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 595–596, that AI regulation should be systemic and include ex ante approval rather than rely on ex post enforcement. Courts may be slow and under-resourced, causal attribution may be difficult, and penalties may not deter serious harms after deployment. This is significant because preventing a dangerous system is often more effective than compensating victims after an opaque and scalable failure. It connects to ex ante approval, ex post enforcement, causation, deterrence, systemic regulation, AI licensing.

AI regulation should be systemic and include ex ante approval rather than rely on ex post enforcement. This matters because preventing a dangerous system is often more effective than compensating victims after an opaque and scalable failure.

ex ante approval
ex post enforcement
causation
deterrence
systemic regulation
AI licensing

Ex post enforcement remains a valuable component of the authors' mixed regime.

Pages 595-596 state the first two domestic principles and the limits of after-the-fact remedies.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 596, that ex ante oversight should cover architecture, objectives, training runs, testing, and deployment. A critical error can enter at any lifecycle stage, as illustrated by a training bug that maximized human disapproval. This is significant because review limited to final products may miss an unsafe objective or irrecoverable training decision. It connects to AI lifecycle, training runs, objective design, testing, deployment review, safety engineering.

ex ante oversight should cover architecture, objectives, training runs, testing, and deployment. This matters because review limited to final products may miss an unsafe objective or irrecoverable training decision.

AI lifecycle
training runs
objective design
testing
deployment review
safety engineering

The OpenAI sign-flip example was a detected research accident, not a deployed catastrophe.

Page 596 maps review across the lifecycle and uses the negative-reward bug as an example.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 596–597, that an AI licensing regime could require justification of safety, fairness, accuracy, transparency, accountability, resilience, and scenario planning. Licenses can govern initial development, new contexts, maintenance, and updates while differentiating beneficial low-risk uses from dangerous ones. This is significant because licensing creates an institutional checkpoint before hard-to-reverse dissemination. It connects to licensing, preapproval, accountability, resilience, risk assessment, model maintenance.

an AI licensing regime could require justification of safety, fairness, accuracy, transparency, accountability, resilience, and scenario planning. This matters because licensing creates an institutional checkpoint before hard-to-reverse dissemination.

licensing
preapproval
accountability
resilience
risk assessment
model maintenance

The article offers dimensions for review but does not designate the licensing agency or precise thresholds.

Pages 596-597 describe the possible functions and criteria of licensure.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 597, that United States AI regulation should be compatible with international strategy but not contingent on prior global agreement. Domestic delay would be especially unwise because frontier research is concentrated and other major jurisdictions are already regulating. This is significant because national leadership can reduce local risk and provide a credible foundation for cooperation. It connects to domestic regulation, international strategy, US leadership, frontier AI, regulatory sequencing, policy coordination.

United States AI regulation should be compatible with international strategy but not contingent on prior global agreement. This matters because national leadership can reduce local risk and provide a credible foundation for cooperation.

domestic regulation
international strategy
US leadership
frontier AI
regulatory sequencing
policy coordination

Unilateral rules can create competitiveness and evasion concerns that international measures must address.

Page 597 states the third principle and its sequencing logic.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 597, that domestic AI legislation can signal commitment and shape international cooperation. The United States can draw from European and Chinese experiments while adopting a broader risk frame. This is significant because early national law can serve as both a laboratory and a diplomatic commitment device. It connects to policy diffusion, international cooperation, EU AI law, Chinese AI law, US legislation, regulatory leadership.

domestic AI legislation can signal commitment and shape international cooperation. This matters because early national law can serve as both a laboratory and a diplomatic commitment device.

policy diffusion
international cooperation
EU AI law
Chinese AI law
US legislation
regulatory leadership

Foreign approaches are partial templates and reflect different political values.

Page 597 connects domestic legislation to international credibility and learning.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 597–598, that hardware export controls and investment restrictions should be evaluated as part of a coherent international AI strategy. The article leaves room for either tighter curbs or a more cooperative policy but insists that domestic legislation and external strategy align. This is significant because compute and capital policy can influence where advanced models are built and who can access them. It connects to export controls, semiconductors, foreign investment, compute governance, China, national security.

hardware export controls and investment restrictions should be evaluated as part of a coherent international AI strategy. This matters because compute and capital policy can influence where advanced models are built and who can access them.

export controls
semiconductors
foreign investment
compute governance
China
national security

The passage does not endorse a particular hardware-control package.

Pages 597-598 identify strategic options and emphasize policy coherence.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 598, that government should fund alignment research and require capability developers to invest materially in safety. Firms have stronger private incentives to improve products than to produce broadly shared safety knowledge. This is significant because public support and regulatory mandates can correct underinvestment in a safety public good. It connects to alignment research, research grants, industry mandates, public goods, AI labs, safety investment.

government should fund alignment research and require capability developers to invest materially in safety. This matters because public support and regulatory mandates can correct underinvestment in a safety public good.

alignment research
research grants
industry mandates
public goods
AI labs
safety investment

Funding alone cannot guarantee that alignment techniques will succeed.

Page 598 states the fourth principle and explains the incentive mismatch.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 598, that AI governance should use diverse and redundant regulatory approaches. Harms arise at different lifecycle stages, and the failure of a single tool should not expose society to catastrophe. This is significant because institutional redundancy is a form of resilience under deep technical uncertainty. It connects to regulatory diversity, defense in depth, redundancy, AI lifecycle, resilience, uncertainty.

AI governance should use diverse and redundant regulatory approaches. This matters because institutional redundancy is a form of resilience under deep technical uncertainty.

regulatory diversity
defense in depth
redundancy
AI lifecycle
resilience
uncertainty

Multiple tools can also generate overlap and compliance cost, which implementation must manage.

Page 598 states the fifth principle and its defense-in-depth rationale.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 598–599, that law should regulate or prohibit AI tools whose primary practical contribution is scalable fraud, harassment, or tortious conduct. Voice cloning and deepfake generators have benign uses but also make impersonation and abuse unusually easy. This is significant because dual-use status does not preclude restrictions when foreseeable harmful use is central and cheap. It connects to voice cloning, deepfakes, dual use, fraud prevention, torts, prohibition.

law should regulate or prohibit AI tools whose primary practical contribution is scalable fraud, harassment, or tortious conduct. This matters because dual-use status does not preclude restrictions when foreseeable harmful use is central and cheap.

voice cloning
deepfakes
dual use
fraud prevention
torts
prohibition

The article does not define the threshold at which harmful utility outweighs protected or beneficial uses.

Pages 598-599 identify obvious harmful pathways under the sixth principle.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 599, that recursive self-improvement, source-code modification, high autonomy, and broad physical tool access warrant special precaution. Preliminary agent systems already write code, generate subgoals, conduct internet research, and operate tools. This is significant because combining self-directed capability growth with external access can make error and misalignment harder to contain. It connects to recursive improvement, autonomous agents, tool access, source-code modification, catastrophic pathways, precaution.

recursive self-improvement, source-code modification, high autonomy, and broad physical tool access warrant special precaution. This matters because combining self-directed capability growth with external access can make error and misalignment harder to contain.

recursive improvement
autonomous agents
tool access
source-code modification
catastrophic pathways
precaution

The authors note that full versions of these systems have not yet been deployed.

Page 599 lists high-risk development patterns and their preliminary analogues.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 599–600, that open release of large AI models can make harmful modification and dissemination effectively irreversible. Users have tuned models on toxic content, generated malware and spam, and connected systems to new tools. This is significant because the social cost of unrestricted weights may differ sharply from the value of open access to smaller vetted systems. It connects to open-source AI, model weights, fine-tuning, malware, disinformation, irreversibility.

open release of large AI models can make harmful modification and dissemination effectively irreversible. This matters because the social cost of unrestricted weights may differ sharply from the value of open access to smaller vetted systems.

open-source AI
model weights
fine-tuning
malware
disinformation
irreversibility

The article preserves a role for smaller vetted open systems and experimentation.

Pages 599-600 give a qualified, risk-tiered argument for caution about open sourcing.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 600, that restrictions may appropriately cover architecture, weights, biases, and some outputs, not only executable products. Technical information can enable reconstruction, harmful fine-tuning, or bypass of controls. This is significant because information governance may be part of containment when disclosure itself transfers dangerous capability. It connects to information hazards, model weights, architecture, output controls, AI security, containment.

restrictions may appropriately cover architecture, weights, biases, and some outputs, not only executable products. This matters because information governance may be part of containment when disclosure itself transfers dangerous capability.

information hazards
model weights
architecture
output controls
AI security
containment

Broad information restrictions raise speech, research, and accountability concerns not resolved in this passage.

Page 600 identifies categories of model information that may require restricted dissemination.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 600, that states should experiment with AI laws alongside federal action. Local and state bans on facial recognition and deepfake abuses can protect residents and generate evidence for other jurisdictions. This is significant because federalism can supply regulatory learning in a field with many uncertain policy options. It connects to federalism, state laboratories, facial recognition, deepfake law, policy experimentation, preemption.

states should experiment with AI laws alongside federal action. This matters because federalism can supply regulatory learning in a field with many uncertain policy options.

federalism
state laboratories
facial recognition
deepfake law
policy experimentation
preemption

State authority may be preempted depending on the future federal statute and the character of the restriction.

Page 600 states the seventh principle and gives examples of state and city action.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 600–601, that civil and constitutional litigation can compensate injuries caused by AI across tort, privacy, medicine, civil rights, fraud, and intellectual property. Traditional causes of action provide immediate avenues while legislatures develop broader governance. This is significant because private enforcement distributes monitoring and gives harmed individuals a direct institutional remedy. It connects to civil litigation, constitutional claims, tort law, privacy, intellectual property, AI harms.

civil and constitutional litigation can compensate injuries caused by AI across tort, privacy, medicine, civil rights, fraud, and intellectual property. This matters because private enforcement distributes monitoring and gives harmed individuals a direct institutional remedy.

civil litigation
constitutional claims
tort law
privacy
intellectual property
AI harms

Existing doctrine may leave causation, standing, proof, and remedy gaps.

Pages 600-601 outline the range of claims through which AI harms may reach courts.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 601, that lawsuits can operate as an early-warning system for dangerous or poorly designed AI. A plaintiff can bring a malfunction to public attention before lobbying produces legislative or agency action. This is significant because decentralized litigation may surface weak signals that centralized regulators have not yet seen. It connects to early warning, private enforcement, discovery, public notice, regulatory lag, AI accidents.

lawsuits can operate as an early-warning system for dangerous or poorly designed AI. This matters because decentralized litigation may surface weak signals that centralized regulators have not yet seen.

early warning
private enforcement
discovery
public notice
regulatory lag
AI accidents

Litigation is reactive and depends on identifiable plaintiffs with viable claims and resources.

Page 601 explains the informational advantage of prompt private claims.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 601, that liability can induce developers to test, secure, study, and document systems before deployment. Expected damages change incentives that otherwise favor speed to market. This is significant because ex post accountability can produce ex ante safety investment. It connects to deterrence, predeployment testing, data security, developer incentives, product liability, AI safety.

liability can induce developers to test, secure, study, and document systems before deployment. This matters because ex post accountability can produce ex ante safety investment.

deterrence
predeployment testing
data security
developer incentives
product liability
AI safety

Liability can also chill beneficial innovation if standards are unclear or damages poorly calibrated.

Page 601 describes the safety behaviors that legal exposure can encourage.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 601–602, that strict liability may fit AI harms because developers often control risk and victims face unusual proof barriers. Existing proposals extend this logic to autonomous systems and other hard-to-observe algorithmic conduct. This is significant because allocating loss to the better risk bearer can improve both compensation and prevention. It connects to strict liability, proof asymmetry, risk bearer, autonomous systems, tort theory, compensation.

strict liability may fit AI harms because developers often control risk and victims face unusual proof barriers. This matters because allocating loss to the better risk bearer can improve both compensation and prevention.

strict liability
proof asymmetry
risk bearer
autonomous systems
tort theory
compensation

The authors report scholarly proposals rather than unequivocally selecting one universal liability rule.

Pages 601-602 summarize the rationale for strict-liability approaches.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 602, that systemic AI liability can reach securities trading and algorithmic collusion as well as physical injury. The regulatory value of doctrine is not confined to robots or consumer products. This is significant because AI can transmit agency and coordination problems across multiple bodies of private and public law. It connects to securities law, antitrust, algorithmic collusion, corporate liability, AI agents, doctrinal adaptation.

systemic AI liability can reach securities trading and algorithmic collusion as well as physical injury. This matters because AI can transmit agency and coordination problems across multiple bodies of private and public law.

securities law
antitrust
algorithmic collusion
corporate liability
AI agents
doctrinal adaptation

The page cites proposals and does not resolve the elements of these claims.

Page 602 identifies securities and antitrust applications of systemic liability thinking.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 602, that courts should prevent litigation from publicly disclosing sensitive model information. Architecture, training methods, benchmark results, and outputs may create security or proliferation risks, making in camera review appropriate. This is significant because transparency for accountability must be balanced against information hazards. It connects to in camera review, trade secrets, information hazards, judicial records, AI security, discovery.

courts should prevent litigation from publicly disclosing sensitive model information. This matters because transparency for accountability must be balanced against information hazards.

in camera review
trade secrets
information hazards
judicial records
AI security
discovery

Secrecy can impede public oversight and plaintiffs' access, so caution must operate case by case.

Page 602 closes the litigation section with a disclosure warning.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 602–603, that AI governance requires an international component because systems, infrastructure, and harms cross borders. Large models may run in distributed facilities, and severe failures can affect global order or humanity. This is significant because national rules alone cannot contain a mobile, replicable technology with transboundary externalities. It connects to international governance, transboundary harm, data centers, global risk, jurisdiction, collective action.

AI governance requires an international component because systems, infrastructure, and harms cross borders. This matters because national rules alone cannot contain a mobile, replicable technology with transboundary externalities.

international governance
transboundary harm
data centers
global risk
jurisdiction
collective action

Domestic regulation remains a necessary starting point despite the need for international measures.

Pages 602-603 state the jurisdictional and physical basis for international governance.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 602–603, that strategic competition can create a regulatory race to the bottom even among states that understand AI danger. Each nation may fear that unilateral caution hands economic or military advantage to a less careful rival. This is significant because shared fear can rationally produce collectively unsafe acceleration without coordination. It connects to race to the bottom, strategic competition, collective action, arms race, AI development, international law.

strategic competition can create a regulatory race to the bottom even among states that understand AI danger. This matters because shared fear can rationally produce collectively unsafe acceleration without coordination.

race to the bottom
strategic competition
collective action
arms race
AI development
international law

The article argues that this dynamic is possible, not unavoidable.

Pages 602-603 frame the international cooperation problem.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 603, that history shows that states sometimes govern dangerous, strategically valuable technologies cooperatively. Just-war rules, pollution controls, scientific governance, pandemic measures, and weapons agreements provide relevant precedents. This is significant because AI competition does not prove international regulation impossible. It connects to international precedent, dangerous technologies, arms control, environmental law, pandemic governance, cooperation.

history shows that states sometimes govern dangerous, strategically valuable technologies cooperatively. This matters because AI competition does not prove international regulation impossible.

international precedent
dangerous technologies
arms control
environmental law
pandemic governance
cooperation

Analogies do not eliminate AI's distinctive speed, opacity, and private-sector role.

Page 603 identifies precedents against a fatalistic race-to-the-bottom view.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 603, that domestic and international regulation can reinforce each other. Successful national measures can become models for international rules, which in turn stimulate further domestic implementation. This is significant because governance may emerge iteratively rather than through a single comprehensive treaty. It connects to policy diffusion, domestic implementation, international norms, regulatory feedback, legal harmonization, institution building.

domestic and international regulation can reinforce each other. This matters because governance may emerge iteratively rather than through a single comprehensive treaty.

policy diffusion
domestic implementation
international norms
regulatory feedback
legal harmonization
institution building

Feedback can also transmit weak or value-laden models, so diffusion is not automatically beneficial.

Page 603 describes the reciprocal relationship between domestic and international action.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 603–604, that AI governance requires a calibrated mixture of transparency and secrecy. Disclosure supports research, monitoring, and accountability but can also spread architectures, methods, capabilities, or reversible outputs. This is significant because the same information can be a safety resource and an information hazard. It connects to transparency, opacity, information hazards, accountability, alignment research, AI security.

AI governance requires a calibrated mixture of transparency and secrecy. This matters because the same information can be a safety resource and an information hazard.

transparency
opacity
information hazards
accountability
alignment research
AI security

The article offers a pluralistic principle rather than a complete disclosure rule.

Pages 603-604 state the benefits and dangers on both sides of the disclosure choice.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 604, that transparency should improve explainability, trace major actors, and share safety failures. Governments should learn about accidents, deceptive behavior, copying attempts, and risky investment so responsible entities can be held accountable. This is significant because visibility into actors and incidents is infrastructure for both learning and enforcement. It connects to explainability, incident reporting, developer accountability, infrastructure providers, AI accidents, safety research.

transparency should improve explainability, trace major actors, and share safety failures. This matters because visibility into actors and incidents is infrastructure for both learning and enforcement.

explainability
incident reporting
developer accountability
infrastructure providers
AI accidents
safety research

Some reports may need to remain regulator-only to avoid spreading dangerous details.

Page 604 identifies the principal governance values served by transparency.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 604–605, that public registries can organize information about AI actors, uses, risks, and compliance. Biosafety and clinical-trial registries show how repositories support decisions, research, trade, and treaty implementation. This is significant because registries can create durable informational infrastructure before full substantive harmonization exists. It connects to public registries, biosafety, clinical trials, compliance, risk information, institutional learning.

public registries can organize information about AI actors, uses, risks, and compliance. This matters because registries can create durable informational infrastructure before full substantive harmonization exists.

public registries
biosafety
clinical trials
compliance
risk information
institutional learning

Registry quality depends on coverage, data accuracy, disclosure design, and enforcement.

Pages 604-605 draw lessons from the Biosafety Clearing-House and ClinicalTrials.gov.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 605–606, that emerging AI registries show that transparency can be adapted to cities, nations, high-risk systems, and scientific research. Municipal registers, China's mandatory filings, proposed European and Pennsylvania databases, and a biomedical registry illustrate multiple designs. This is significant because governance can begin with targeted registries rather than await one universal global database. It connects to algorithm registries, municipal AI, China, European Union, biomedical research, regulatory experimentation.

emerging AI registries show that transparency can be adapted to cities, nations, high-risk systems, and scientific research. This matters because governance can begin with targeted registries rather than await one universal global database.

algorithm registries
municipal AI
China
European Union
biomedical research
regulatory experimentation

The examples differ sharply in public access, legal force, and substantive scope.

Pages 605-606 survey current and proposed AI registry models.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 606, that sensitive AI disclosures can be confined to trusted regulators rather than made public. The IAEA demonstrates that an international institution can inspect confidential information while limiting wider release. This is significant because accountability does not always require publishing capability-enhancing details to everyone. It connects to confidential disclosure, IAEA, regulatory access, information security, international inspection, AI oversight.

sensitive AI disclosures can be confined to trusted regulators rather than made public. This matters because accountability does not always require publishing capability-enhancing details to everyone.

confidential disclosure
IAEA
regulatory access
information security
international inspection
AI oversight

AI information may be easier to copy and harder to detect than nuclear material.

Page 606 uses the IAEA as a model for regulator-only access.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 606–607, that harmonized AI rules can reduce races to the bottom, relocation to weak jurisdictions, trade conflict, and duplicative burdens. Common requirements also enable countries to learn from each other's decisions and evidence. This is significant because coordination can improve both safety and administrability in a transnational industry. It connects to legal harmonization, regulatory arbitrage, trade, multinational firms, race to the bottom, AI standards.

harmonized AI rules can reduce races to the bottom, relocation to weak jurisdictions, trade conflict, and duplicative burdens. This matters because coordination can improve both safety and administrability in a transnational industry.

legal harmonization
regulatory arbitrage
trade
multinational firms
race to the bottom
AI standards

Harmonization can entrench weak standards or suppress beneficial national experimentation.

Pages 606-607 explain the purposes and mechanisms of regulatory harmonization.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 607, that registries and model standards can promote convergence without identical national statutes. Biosafety information sharing and pharmaceutical standards show how common evidence and expert specifications influence domestic decisions. This is significant because international coordination can proceed through shared inputs and benchmarks as well as binding commands. It connects to model standards, information sharing, pharmaceutical regulation, biosafety, regulatory convergence, technical expertise.

registries and model standards can promote convergence without identical national statutes. This matters because international coordination can proceed through shared inputs and benchmarks as well as binding commands.

model standards
information sharing
pharmaceutical regulation
biosafety
regulatory convergence
technical expertise

Model standards may lack democratic input and do not guarantee enforcement.

Page 607 identifies two comparatively flexible harmonization tools.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 607–608, that international technology assessments can identify risks, engage publics, and reshape development trajectories. Shared assessments give policymakers a common evidentiary base and may support later harmonization. This is significant because governance benefits from organized learning before legal consensus is complete. It connects to technology assessment, public engagement, risk analysis, international cooperation, adaptive governance, evidence base.

international technology assessments can identify risks, engage publics, and reshape development trajectories. This matters because governance benefits from organized learning before legal consensus is complete.

technology assessment
public engagement
risk analysis
international cooperation
adaptive governance
evidence base

Assessment informs but does not itself compel regulation.

Pages 607-608 state the functions of assessment in emerging-technology governance.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 608, that OECD assessments of genetically modified organisms show the value and limits of consensus documents. They harmonize information and influence standards even though countries retain divergent substantive rules. This is significant because shared technical knowledge can reduce friction without resolving political disagreement. It connects to OECD, GMO governance, consensus documents, risk assessment, soft coordination, regulatory diversity.

OECD assessments of genetically modified organisms show the value and limits of consensus documents. This matters because shared technical knowledge can reduce friction without resolving political disagreement.

OECD
GMO governance
consensus documents
risk assessment
soft coordination
regulatory diversity

The GMO experience suggests assessments are necessary but insufficient for harmonization.

Page 608 explains the OECD model and its incomplete effect on national policy.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 608–609, that an international AI agency could combine assessment, expert convening, technical tools, and rapid guidance. Proposals for an IAAI and the United Nations advisory body seek scientific consensus and coordinated responses to emerging problems. This is significant because a standing institution can convert episodic expert concern into continuing governance capacity. It connects to international AI agency, United Nations, expert consensus, misinformation, technical guidance, institutional capacity.

an international AI agency could combine assessment, expert convening, technical tools, and rapid guidance. This matters because a standing institution can convert episodic expert concern into continuing governance capacity.

international AI agency
United Nations
expert consensus
misinformation
technical guidance
institutional capacity

The article presents proposals whose authority, funding, and enforcement remain unsettled.

Pages 608-609 describe proposed international assessment institutions.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 609, that soft law offers speed, flexibility, and international reach but lacks direct enforceability. Principles, codes, certifications, audits, and standards can arise faster than treaties yet may attract compliance mainly from responsible actors. This is significant because soft law is useful for a rapidly changing field but cannot safely substitute for binding rules where incentives diverge. It connects to soft law, codes of conduct, certification, voluntary compliance, international standards, accountability.

soft law offers speed, flexibility, and international reach but lacks direct enforceability. This matters because soft law is useful for a rapidly changing field but cannot safely substitute for binding rules where incentives diverge.

soft law
codes of conduct
certification
voluntary compliance
international standards
accountability

The article treats soft law as one layer in a broader regime, not a complete solution.

Page 609 defines soft law and balances its advantages against participation and enforcement limits.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 609–610, that procurement, insurance, publication, and incorporation into domestic law can give soft standards practical force. The Helsinki research guidelines became influential through indirect institutional adoption despite lacking direct legal status. This is significant because nonbinding norms can become consequential when access to markets, insurance, funding, or publication depends on compliance. It connects to indirect enforcement, procurement, insurance, medical ethics, publication standards, soft law.

procurement, insurance, publication, and incorporation into domestic law can give soft standards practical force. This matters because nonbinding norms can become consequential when access to markets, insurance, funding, or publication depends on compliance.

indirect enforcement
procurement
insurance
medical ethics
publication standards
soft law

Indirect enforcement may be uneven and can shift rulemaking power to private gatekeepers.

Pages 609-610 explain how soft law can acquire real-world leverage.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 610–611, that OECD and UNESCO principles establish broad international norms for trustworthy and ethical AI. They call for safety, privacy, transparency, explainability, and broad social benefit and have gained wide formal adoption. This is significant because general principles can establish vocabulary and expectations across jurisdictions before detailed law converges. It connects to OECD AI principles, UNESCO, trustworthy AI, privacy, explainability, international norms.

OECD and UNESCO principles establish broad international norms for trustworthy and ethical AI. This matters because general principles can establish vocabulary and expectations across jurisdictions before detailed law converges.

OECD AI principles
UNESCO
trustworthy AI
privacy
explainability
international norms

Their generality makes implementation difficult to measure and operationalize.

Pages 610-611 describe prominent intergovernmental soft-law initiatives and their reach.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 611, that private multi-stakeholder AI principles broaden participation but often remain aspirational. The Partnership on AI joins industry, academia, civil society, and media around high-level pillars and tenets. This is significant because plural participation can build legitimacy and expertise, but broad language may conceal disagreement about concrete duties. It connects to multi-stakeholder governance, Partnership on AI, industry standards, civil society, AI ethics, operationalization.

private multi-stakeholder AI principles broaden participation but often remain aspirational. This matters because plural participation can build legitimacy and expertise, but broad language may conceal disagreement about concrete duties.

multi-stakeholder governance
Partnership on AI
industry standards
civil society
AI ethics
operationalization

Participation does not guarantee compliance by actors posing the greatest risk.

Page 611 uses the Partnership on AI to illustrate private soft-law initiatives.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 611–612, that technical standards translate general AI ethics into more specific management, governance, bias, transparency, and risk practices. ISO, IEEE, NIST, and the G7 have issued or developed frameworks that increasingly inform domestic regulation. This is significant because standards can provide implementable baselines while legislation remains general. It connects to ISO 42001, IEEE standards, NIST AI RMF, G7 code, technical standards, risk management.

technical standards translate general AI ethics into more specific management, governance, bias, transparency, and risk practices. This matters because standards can provide implementable baselines while legislation remains general.

ISO 42001
IEEE standards
NIST AI RMF
G7 code
technical standards
risk management

Standards processes may be closed, lack democratic legitimacy, or remain voluntary.

Pages 611-612 survey technical standards and their growing regulatory role.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 612–613, that treaties can impose binding AI obligations but are slow to negotiate, hard to amend, and often weakly enforced against private actors. A procedural treaty could begin with registration, monitoring, and audit duties before adding substantive standards. This is significant because hard law offers commitment value but must be designed for a technology that changes faster than conventional treaty processes. It connects to treaties, hard law, monitoring, private actors, procedural obligations, adaptive governance.

treaties can impose binding AI obligations but are slow to negotiate, hard to amend, and often weakly enforced against private actors. This matters because hard law offers commitment value but must be designed for a technology that changes faster than conventional treaty processes.

treaties
hard law
monitoring
private actors
procedural obligations
adaptive governance

Treaty labels do not guarantee effective domestic implementation or enforcement.

Pages 612-613 describe the potential sequence and structural limits of hard law.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 613, that national AI regimes generally fall into existing-law, risk-tiering, or preapproval approaches. These categories provide practical building blocks for international comparison and eventual convergence. This is significant because global governance will likely emerge from concrete domestic models rather than abstract agreement alone. It connects to comparative AI law, risk tiers, existing law, preapproval, policy diffusion, international standards.

national AI regimes generally fall into existing-law, risk-tiering, or preapproval approaches. This matters because global governance will likely emerge from concrete domestic models rather than abstract agreement alone.

comparative AI law
risk tiers
existing law
preapproval
policy diffusion
international standards

Real systems are hybrids and may not fit neatly into one category.

Page 613 introduces the comparative analysis of national approaches.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 613–614, that the United Kingdom's context-specific, existing-law approach shows political interest but misses systemic risks. Principles, technical standards, and sector regulators can govern uses without directly supervising AI as a general technology. This is significant because application-level flexibility may preserve innovation yet leave shared upstream hazards unaddressed. It connects to United Kingdom, principles-based regulation, sector regulators, existing law, systemic risk, AI policy.

the United Kingdom's context-specific, existing-law approach shows political interest but misses systemic risks. This matters because application-level flexibility may preserve innovation yet leave shared upstream hazards unaddressed.

United Kingdom
principles-based regulation
sector regulators
existing law
systemic risk
AI policy

The assessment reflects the policy described in the article and may not capture later legal developments.

Pages 613-614 describe and critique the United Kingdom's approach.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 614–615, that the European Union's tiered AI Act combines prohibitions, high-risk duties, transparency rules, and lighter treatment of low-risk uses. Its categories demonstrate a more interventionist and differentiated model than reliance on existing law alone. This is significant because risk classification can calibrate legal burdens to expected harm while preserving broad deployment. It connects to EU AI Act, risk-based regulation, prohibited practices, high-risk systems, transparency, general-purpose AI.

the European Union's tiered AI Act combines prohibitions, high-risk duties, transparency rules, and lighter treatment of low-risk uses. This matters because risk classification can calibrate legal burdens to expected harm while preserving broad deployment.

EU AI Act
risk-based regulation
prohibited practices
high-risk systems
transparency
general-purpose AI

The article describes the political agreement and developing text available in 2023-2024.

Pages 614-615 summarize the Act's four risk levels and general-purpose AI obligations.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 615, that the EU approach still leaves military systems, alignment, and recursive self-improvement insufficiently governed. Its application-focused structure omits several pathways central to the article's systemic account. This is significant because even ambitious risk legislation can have blind spots if its taxonomy is not technology-wide. It connects to EU AI Act, military exemption, AI alignment, recursive improvement, regulatory gaps, systemic oversight.

the EU approach still leaves military systems, alignment, and recursive self-improvement insufficiently governed. This matters because even ambitious risk legislation can have blind spots if its taxonomy is not technology-wide.

EU AI Act
military exemption
AI alignment
recursive improvement
regulatory gaps
systemic oversight

The critique targets the version discussed in the source and may need updating as implementation evolves.

Page 615 identifies the Act's principal omissions from the authors' perspective.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 615–616, that China's generative-AI rules impose registration, data-quality, security-assessment, privacy, transparency, and content duties on public-facing providers. The model is restrictive and explicitly links AI output to state-defined social values. This is significant because strong ex ante controls are institutionally possible but can protect state ideology as readily as public safety. It connects to China, generative AI regulation, security assessment, training data, content control, public-facing services.

China's generative-AI rules impose registration, data-quality, security-assessment, privacy, transparency, and content duties on public-facing providers. This matters because strong ex ante controls are institutionally possible but can protect state ideology as readily as public safety.

China
generative AI regulation
security assessment
training data
content control
public-facing services

The regime reflects a different political system and applies only within specified private-sector contexts.

Pages 615-616 describe China's interim generative-AI measures.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 616, that China's rules exclude internal and governmental AI use, leaving national-security and surveillance risks insufficiently constrained. A regime can tightly control public providers while giving the state broad freedom to deploy AI. This is significant because regulatory stringency should be evaluated by whose conduct is governed, not only by the burden imposed on firms. It connects to government AI, surveillance, national security, regulatory scope, China, state accountability.

China's rules exclude internal and governmental AI use, leaving national-security and surveillance risks insufficiently constrained. This matters because regulatory stringency should be evaluated by whose conduct is governed, not only by the burden imposed on firms.

government AI
surveillance
national security
regulatory scope
China
state accountability

The article reports observer concern and does not provide a complete account of Chinese public law.

Page 616 highlights the public-sector exclusion and its implications.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 616, that an ICAO-style institution could coordinate technical standards where harmonization matters more than coercive inspection. Civil aviation offers a softer model of international governance built around standards and recommended practices. This is significant because different AI domains may require institutions with different levels of enforcement power. It connects to ICAO, international standards, civil aviation, harmonization, AI institutions, sectoral governance.

an ICAO-style institution could coordinate technical standards where harmonization matters more than coercive inspection. This matters because different AI domains may require institutions with different levels of enforcement power.

ICAO
international standards
civil aviation
harmonization
AI institutions
sectoral governance

The analogy does not establish that aviation's compliance mechanisms transfer directly to AI.

Page 616 presents ICAO as a model for coordination-oriented governance.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 616–617, that an IAEA-style AI body could inspect systems, require audits, test compliance, and restrict dangerous deployment. This stronger model fits contexts in which preventing catastrophic risk dominates ordinary coordination concerns. This is significant because high-consequence AI may require verification and enforcement rather than voluntary convergence alone. It connects to IAEA, international inspection, AI audits, safety standards, deployment limits, existential risk.

an IAEA-style AI body could inspect systems, require audits, test compliance, and restrict dangerous deployment. This matters because high-consequence AI may require verification and enforcement rather than voluntary convergence alone.

IAEA
international inspection
AI audits
safety standards
deployment limits
existential risk

AI lacks nuclear material's distinctive physical footprint and involves many more private actors and sectors.

Pages 616-617 describe the proposed IAEA analogy and begin identifying its limits.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 617, that AI oversight is more difficult than nuclear governance because computation is diffuse, private activity is central, and development is rapid. Distributed training may evade detection, and institutions have less time to evolve through decades of revealed gaps. This is significant because international design must adapt precedents rather than copy them mechanically. It connects to distributed compute, private actors, nuclear analogy, verification, regulatory speed, institutional design.

AI oversight is more difficult than nuclear governance because computation is diffuse, private activity is central, and development is rapid. This matters because international design must adapt precedents rather than copy them mechanically.

distributed compute
private actors
nuclear analogy
verification
regulatory speed
institutional design

Large compute facilities can still leave detectable footprints, so opacity is not absolute.

Page 617 specifies the major disanalogies between AI and the IAEA's subject matter.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 617, that international AI governance can operate through state obligations even without a powerful global regulator. The Council of Europe convention illustrates a treaty model centered on privacy, transparency, auditability, safety, and security duties implemented nationally. This is significant because binding coordination can be decentralized through domestic implementation. It connects to Council of Europe, AI treaty, human rights, auditability, domestic implementation, international law.

international AI governance can operate through state obligations even without a powerful global regulator. This matters because binding coordination can be decentralized through domestic implementation.

Council of Europe
AI treaty
human rights
auditability
domestic implementation
international law

At the source date the convention had been adopted but had not yet opened for signature or accumulated implementation experience.

Page 617 describes the 2024 Council of Europe framework convention.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 618, that registries, principles, standards, and domestic laws are accreting into an international AI governance network. Connections among institutions can support information sharing, coordination, and norm formation before a single global authority exists. This is significant because polycentric governance may be a realistic route to stronger international rules. It connects to governance networks, polycentric regulation, norm building, information sharing, technical standards, international cooperation.

registries, principles, standards, and domestic laws are accreting into an international AI governance network. This matters because polycentric governance may be a realistic route to stronger international rules.

governance networks
polycentric regulation
norm building
information sharing
technical standards
international cooperation

A network can remain fragmented or weak unless actors build meaningful connections and accountability.

Page 618 describes the emerging institutional network and its promise.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 618–619, that comprehensive regulation is necessary to realize AI's benefits while addressing present harm, future disruption, misuse, and misalignment. The article combines domestic principles, litigation, international cooperation, and calibrated secrecy into one program. This is significant because the risk taxonomy and institutional design are mutually supporting parts of the same systemic thesis. It connects to comprehensive regulation, AI benefits, misalignment, domestic governance, international cooperation, risk taxonomy.

comprehensive regulation is necessary to realize AI's benefits while addressing present harm, future disruption, misuse, and misalignment. This matters because the risk taxonomy and institutional design are mutually supporting parts of the same systemic thesis.

comprehensive regulation
AI benefits
misalignment
domestic governance
international cooperation
risk taxonomy

The article begins a policy conversation and does not claim to solve every implementation problem.

Pages 618-619 synthesize the article's substantive and institutional conclusions.


Professor Yonathan Arbel claims, in the coauthored article “Systemic Regulation of Artificial Intelligence” on pages 619, that epistemic humility supports robust systemic regulation rather than passivity. When the safety of a powerful system is deeply uncertain, the authors analogize deployment to boarding a plane of unknown safety. This is significant because uncertainty about unknown unknowns can justify institutional caution against market pressure for fragmented rules. It connects to epistemic humility, unknown unknowns, systemic regulation, market pressure, precaution, AI governance.

epistemic humility supports robust systemic regulation rather than passivity. This matters because uncertainty about unknown unknowns can justify institutional caution against market pressure for fragmented rules.

epistemic humility
unknown unknowns
systemic regulation
market pressure
precaution
AI governance

The plane analogy is rhetorical and does not itself specify the optimal degree of regulation.

Page 619 closes with the uncertainty analogy and rejects assurances based only on responsible intent.