Algorithmic Power and Constitutional Control / Reflections on Reading the Global AI Code for the Marketplace 2026

I. The Demand for Governance Beyond Compliance

The challenge facing corporate executives is no longer merely achieving compliance with laws and regulations regarding AI. The focus has shifted to how to govern technology that involves autonomous decision-making and how to balance the protection of fundamental human rights with business activities.

The "Global AI Code for the Marketplace 2026" (Version 1.0, hereinafter "the Code"), published by Bluefox Global Consulting Services on March 2, 2026, seeks the foundation of AI governance in the history of human power control and translates those principles into specific corporate organizational procedures [Note 1]. The issuer is a private consulting firm based in Virginia, USA, and the 151-page document is structured into three parts: a principles code, an operational framework for organizations, and an implementation procedure manual.

The core problem setting of the Code is clear. AI has become an entity that exerts concentrated influence on individual opportunities and autonomy, such as in loan eligibility, hiring screening, medical diagnosis, and involvement in law enforcement, and the question is how to build a governance structure for it. We will examine below the document's attempt to construct governance from the context of constitutionalism, viewing AI not as a technology with specific functions, but as an algorithmic power that intervenes in people's lives.

1. Visualization of Power and Proportional Constraints

Just as the Code of Hammurabi around 1750 BC visualized state power through written law, and the United States Constitution (1787) established checks and balances against government power, the argument that corresponding constraints are necessary for the decision-making power held by AI has a certain persuasiveness as a continuous line in the history of legal thought [Note 2].

The "three-layer constitutional model" presented by the Code is an institutional extension of this argument. It is a structure that places constitutional texts defining high-level principles and rights (such as the OECD AI Principles, UNESCO AI Ethics Recommendations, and the White House AI Bill of Rights) in the first layer, regulations defining obligations according to risk (such as the EU AI Act, NIST AI Risk Management Framework, and ISO/IEC 42001) in the second layer, and enforcement agencies (such as regulatory authorities, audit bodies, and ethics committees) in the third layer [Note 3]. The adoption of a design that inherits the concept of proportional justice seen in ancient codes and changes the weight of obligations in stages according to the potential risks of the system can be evaluated as an approach that repositions the discussion of AI ethics, which tends to end in abstraction, as a problem of institutional design.

2. Risk Classification and High-Risk Obligations

Article 3 of the Code classifies AI systems into four levels: prohibited risk, high risk, limited risk, and minimal risk. For high-risk systems, obligations such as the establishment of a risk management system, data governance, technical documentation, human oversight, and post-market monitoring are imposed. This classification, which substantially corresponds to the framework of the EU AI Act (Regulation (EU) 2024/1689), is consistent with existing regulatory systems and is structured to be easy for compliance officers to reference [Note 4].

II. Dynamic Monitoring of Agentic AI

Beyond presenting ideals, the implementation procedure manual of the Code reflects the technological situation as of 2026 and is characterized in practice by incorporating control mechanisms for agentic AI that autonomously perform tasks.

1. Boundary Setting According to Autonomy Levels

The manual classifies AI autonomy into four levels, from A to D. These are distinguished as Level A, which is limited to mere suggestions; Level B, which executes actions after prior human approval; Level C, which allows limited autonomous action; and Level D, which takes extensive autonomous action. For AI at a level that intervenes in systems or external functions without human approval, boundary setting that defines access rights to available tools in advance is strongly required. The attitude of attempting to control the uncertainty accompanying the improvement of autonomy through the limitation of authority and post-monitoring procedures is considered a reasonable practical response.

2. Behavioral Deviation and Parallel Operation Testing

For agentic AI with high autonomy, the implementation of shadow testing, where it is operated in parallel with existing business processes before being introduced into the production environment, is required. In addition, technical control means are specified, such as monitoring for behavioral drift, which issues an alert when behavioral patterns deviate from prior assumptions to a certain extent after operation begins. At the stage where AI plans and executes actions on its own, static risk assessment at the time of system development no longer makes sense. We interpret the formalization of dynamic monitoring during operation and mechanisms for stripping authority in emergencies as operational procedures as a realistic control method adapted to the development of technology.

III. Extension of Responsibility in the Supply Chain

The value chain due diligence clause in Article 6 of the Code does not limit the organization's control to the inside of the AI system, but extends it to the entire broad network of transactions involved in development and operation [Note 5]. It adopts the OECD due diligence framework, a structure that determines whether an organization's activities are "causing," "contributing to," or "directly linked to" adverse impacts, and requires corrective measures and compensation according to the degree of involvement.

The scope of evaluation includes not only the direct harm caused by the system, but also the psychological burden on workers responsible for classifying training data and the negative environmental impact associated with the operation of computational resources. This design, which requires correction according to the degree of involvement not only when one's own actions cause direct harm but also when one has facilitated harm caused by others, extends the scope of corporate responsibility to the entire supply chain and significantly increases the operational burden on the provider side. On the other hand, the three-layer governance structure [Note 6], which places the CEO at the top and finely allocates governance duties to officers in charge of legal, human resources, data management, information security, etc., is a design that disperses AI control into the organization's overall decision-making structure rather than pushing it onto a specific department, and the effect of internal organizational checks can be expected by intersecting the authority and responsibility of each officer.

IV. Issues Regarding the Normative Authority of the "Global Code"

As long as we are discussing this Code, we must also touch upon the source of its normative authority. The disclaimer at the beginning of the document clearly states that the document is provided for "general information purposes only" and "does not constitute any professional, legal, or technical advice." There is no problem with the disclaimer itself, but there is considerable tension between it and the name "Global Code".

The EU AI Act holds legal binding force because it has undergone legislative procedures by democratically legitimate institutions, namely the European Parliament and the Council of the European Union, while the OECD AI Principles derive their normative influence from the multilateral consensus adopted by the governments of OECD member countries. This Code is a document voluntarily published by a private consulting firm and lacks the procedural legitimacy to guarantee 'global' normativity.

I do not intend to point this out as a flaw. Rather, the movement of the private sector to formulate and publish voluntary governance norms can be duly evaluated as a form of soft law formation. The issue lies in the overestimation of norms that arises when such a document bears the title of a 'Global Code,' and conversely, it also highlights how difficult it is to construct a true global AI governance system. The OECD AI Principles, UNESCO Recommendation on the Ethics of AI, NIST AI RMF, ISO/IEC 42001, and the EU AI Act, all of which this Code references, function as independent normative documents. While I do not deny the practical value of this Code in reconstructing these in a way that makes them easier to reference in an integrated manner, the document does not necessarily make clear what its unique normative contribution is beyond existing frameworks.

V. In Conclusion

The concept of this Code, which applies the logic of power control dating back to ancient legal codes to AI and translates it into implementation procedures for corporate organizations, once again raises the question of what values an organization is protecting by controlling AI. As technology increases its autonomy and its influence reaches the ends of supply chains, sophisticated institutional design capable of counterbalancing such high levels of autonomy is required. I hope this serves as an opportunity to think about how to incorporate controls based on human rights and democratic values into the daily decision-making processes of organizations. For details, please refer to the original document.

(Magazine) "AI and Law - Reflections"

*Please refer to the following for the table of contents

note General Terms and Conditions Article 3, Paragraph 2, First Sentence
3.2 The copyright of digital content created by the creator belongs to the creator.