By Diana Mosquera
The State of AI Governance: From Theoretical Ethics to Technical Reality
In recent years, the governance of artificial intelligence has made significant progress in the formulation of ethical principles, regulatory frameworks, and multi-stakeholder dialogue forums. However, these advances coexist with some increasingly evident gaps: there is a deep divide between what is promised in terms of transparency, accountability, and fairness, and what can actually be verified in practice [1][4]. This gap is not accidental. It stems from a structural limitation: we are attempting to govern systems that we do not yet fully understand.
Governance Beyond Regulation
AI governance cannot be reduced to a list of prohibitions or regulatory principles. Regulating without understanding is, at best, inefficient; at worst, a form of symbolic bureaucracy. True governance begins with a deep technical understanding: mapping the architecture of the models, their training data, their biases, and their inference logic [2]. Without this foundation, regulation lacks any real capacity for intervention. Governance implies the ability to audit. It implies having the tools to open what is currently presented as a “black box.” In this sense, AI governance is not merely a political or legal issue. It is also an engineering challenge.
The Challenge of Technical Governance
For governance to be effective, we need a technical infrastructure that remains incomplete today. This emerging field known as Technical AI Governance (TAIG) raises some of the most critical issues [6][3]:
- How can we verify that a model has not been trained on sensitive or protected data if we do not have access to the dataset?
- How can we ensure that a system can “forget” specific information without compromising its performance?
- How can we track where and how computing power is being used in a context of geopolitical tensions?
Solutions such as membership inference attacks, machine unlearning, or hardware geolocation aim to close these gaps. But we are still far from having mature tools that allow regulators, researchers, or civil society to independently audit these systems.
AI as a socio-technical ecology
Added to this technical complexity is another dimension that is often overlooked: AI is not just software. It is a global ecology that depends on deeply unequal flows of resources, labor, and knowledge [2]. Much of current governance focuses on “downstream” impacts: the safety of end users, harmful content, and immediate risks. However, the most invisible and often more structural impacts occur “upstream”: mineral extraction, intensive energy and water consumption, or precarious content moderation work, concentrated in the Global South. Ignoring these layers limits governance to the surface of the problem.
Effective governance must, therefore, adopt a systemic approach: recognizing AI as a global socio-technical infrastructure and moving toward governance models that are also pluriversal—that is, capable of adapting to different ecological, cultural, and political contexts.