IAMirror

A successful Global Dialogue on AI Governance should move beyond high-level principles and deliver operational clarity. First, it should recognize that governance does not start at the level of AI systems, but upstream—at the level of human decision-making under complexity, uncertainty, and systemic risk. Without structured reasoning, even well-regulated AI systems can lead to misaligned outcomes. Second, the Dialogue should promote practical, non-prescriptive frameworks that help actors—governments, institutions, and developers—structure what is known, unknown, and interpreted before decisions are made. This would strengthen clarity, reduce cognitive bias, and improve traceability in high-stakes environments. Third, success requires translating principles into real-world experimentation. Pilot-based approaches, particularly in the Global South, should be encouraged to test governance concepts in sectors such as agriculture, health, and infrastructure, where uncertainty and impact are highest. Fourth, the Dialogue should facilitate continuous exchange between institutional actors and field-level initiatives, ensuring that governance evolves through practice as well as policy. Finally, a successful outcome would be the recognition that effective AI governance depends not only on regulating systems, but on improving the quality of human decision processes interacting with them

• Safe, secure and trustworthy AI
• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Transparency, accountability, and human oversight

The selected priorities reflect a central premise: effective AI governance depends not only on regulating systems, but on strengthening the quality of human decision-making interacting with them. AI that is safe, secure and trustworthy is essential, but safety cannot rely solely on technical robustness. It also depends on how decisions are framed, interpreted, and acted upon by human actors. Capacity building in AI is therefore critical. However, beyond technical skills, there is a need to develop structured reasoning capabilities-particularly in contexts of uncertainty, limited data, and systemic risk, which are common in the Global South. Transparency, accountability and human oversight are directly linked to the ability to make decision processes explicit. Without structured approaches, human oversight remains theoretical rather than operational. Social, economic, ethical and cultural implications are central because AI systems interact with diverse realities. In many contexts, especially in agriculture, valuable knowledge is empirical, local, and sometimes transmitted informally. Integrating these dimensions into structured frameworks is key to inclusive and sustainable governance. Together, these priorities point toward a complementary perspective: governance should not only define how AI systems behave, but also support how humans organize what they know, do not know, and interpret before making decisions

Yes. A critical transversal issue that remains largely unaddressed is not only how AI systems are designed, but how human reasoning is structured before interacting with them. The introduction of calculators did not replace mathematical reasoning; it required it to be structured beforehand. Without understanding what to compute, the tool had limited value. A similar dynamic is emerging with AI. Highly advanced systems are now widely accessible, yet they are often used without structured reasoning processes. As a result, AI does not only amplify intelligence-it can also amplify confusion, bias, or poorly framed questions. The issue is therefore not limited to what AI produces, but to the quality of what it is asked to process. This reveals a methodological gap. While governance frameworks increasingly regulate models and outputs, there is still no widely adopted approach to help individuals and institutions structure what they know, what they do not know, and what they interpret before making decisions with AI. Without this layer, there is a structural risk: systems may optimize decisions that were never clearly structured in the first place. An emerging priority, therefore, is the development of cognitive frameworks that improve the quality of human input and make reasoning explicit before interaction with AI systems. In that sense, AI should not only be seen as an automation tool, but as a potential cognitive mirror-capable of reflecting the structure of human reasoning, rather than merely accelerating it. The shift is simple but fundamental: structured thinking before interaction determines the quality of outcomes. Addressing this gap could significantly strengthen the effectiveness, safety, and relevance of AI governance across contexts. A conceptual framework exploring this approach is available for reference: https://doi.org/10.5281/zenodo.19375575

In Latin America, and particularly in Colombia, gaps in AI governance are not only regulatory or technological—they are deeply operational and cognitive. One of the main challenges is that AI adoption is accelerating faster than the capacity of institutions, producers, and decision-makers to structure their reasoning when using these systems. This creates a situation where powerful tools are available, but their use remains inconsistent, intuitive, or unstructured. In sectors such as agriculture, this gap is particularly visible. Small and medium producers are increasingly exposed to data, recommendations, and digital tools, yet often lack structured methods to interpret this information and translate it into reliable decisions. As a result, there is a risk of amplifying uncertainty rather than reducing it. At the same time, this represents a major opportunity. Regions like Latin America have strong empirical knowledge systems, including ancestral and field-based practices, which are often underutilized in current AI frameworks. Structuring this knowledge and combining it with AI could significantly improve decision quality, productivity, and sustainability. Another key opportunity lies in developing methodologies that help decision-makers distinguish between what they know, what they do not know, and what they interpret before interacting with AI systems. This is particularly relevant in high-uncertainty environments, where decisions have economic, environmental, and social impact. Ultimately, strengthening AI governance in the region requires not only better regulation, but also better cognitive frameworks that improve how humans engage with AI. Bridging this gap could enable more inclusive, context-aware, and effective use of AI across sectors.

The Global Dialogue on AI can play a critical role by shifting international cooperation from abstract alignment principles to operational coherence in real-world decision-making. Today, most cooperation efforts focus on standards, ethics, and regulatory convergence. While essential, these approaches often remain disconnected from how decisions are actually made under uncertainty across different contexts, cultures, and sectors. The Dialogue can act as a bridge by enabling shared methodologies that improve how actors structure information before using AI systems. This includes fostering common approaches to distinguish what is known, unknown, and interpreted, especially in environments where data is incomplete and decisions carry systemic consequences. Such an approach would not impose uniform solutions, but rather create a shared cognitive infrastructure for decision-making, adaptable to local realities. This is particularly relevant for regions like Latin America, where diversity of contexts requires flexible and context-aware governance. In addition, the Dialogue can facilitate exchanges between empirical knowledge systems—such as agricultural or community-based practices—and advanced AI capabilities, allowing cooperation to move beyond technology transfer toward mutual enrichment. Ultimately, the role of the Dialogue is not only to align systems, but to align how humans think and decide when using them. AI governance is not only about how systems behave, but about how humans structure decisions before using them.

The Global Dialogue on AI should build upon existing international initiatives led by multilateral organizations, standards bodies, and regional governance frameworks, including those within the United Nations system and other global partnerships. These initiatives have established essential foundations in ethics, regulation, and technical standards. However, their primary focus remains on systems, compliance, and outputs. A key opportunity for the Dialogue is to complement these efforts by addressing a less developed dimension: the structure of human reasoning in interaction with AI. To do so, the Dialogue could connect with academic institutions, applied research centers, and field-based pilot initiatives across sectors such as agriculture, education, and public policy. These environments provide critical insights into how AI is actually used in practice, particularly in contexts with high uncertainty and limited resources. The added value of the Dialogue would be to integrate these perspectives into an operational layer of governance—one that supports decision-makers in structuring their inputs, not only evaluating outputs. This could take the form of shared methodological frameworks, pilot programs, and cross-regional experimentation platforms that test how structured reasoning improves the reliability and impact of AI-assisted decisions. By doing so, the Dialogue would not duplicate existing efforts, but enhance them—linking governance principles with real-world decision processes. Better coordination of systems is not sufficient without better structuring of the decisions that guide them.

Different stakeholders can contribute to the AI Dialogue not only through perspectives, but through the quality of how those perspectives are structured. Governments, private sector actors, academia, and civil society already bring valuable inputs. However, the challenge is not the lack of contributions, but the lack of shared structure to make these contributions comparable, actionable, and coherent. A more effective Dialogue would therefore benefit from formats that go beyond opinion-sharing and move toward structured reasoning. For example, contributions could be framed around three simple dimensions: what is known, what is uncertain, and what is interpreted. This would allow diverse actors to express their insights in a way that is both rigorous and adaptable across contexts. In addition, the Dialogue could integrate iterative and practice-based formats, where ideas are tested through real-world pilots and feedback loops, rather than remaining at the level of static recommendations. The objective is not to standardize viewpoints, but to make them structurally intelligible across sectors and regions. The value of a dialogue is not only in what is said, but in how clearly it can be understood, compared, and used.

Many voices remain underrepresented in global AI governance debates, particularly those operating in high-uncertainty, resource-constrained, and field-based environments. This includes small-scale producers, local communities, practitioners, and actors whose knowledge is primarily empirical rather than formalized. These perspectives are critical because they engage with complexity in real conditions, where decisions must be made without complete data and with immediate consequences. However, their contributions are often excluded due to lack of formal frameworks to express their reasoning in a way that is recognized within institutional discussions. Inclusion, therefore, is not only about access, but about translation. It requires mechanisms that allow different forms of knowledge to be structured, articulated, and understood across contexts. This could involve developing simple but robust frameworks that help individuals express what they observe, what they infer, and what remains uncertain. By doing so, empirical knowledge can become interoperable with scientific and technical approaches. Inclusion is not achieved when everyone speaks, but when different forms of knowledge can be understood and integrated.

To foster meaningful and dynamic engagement, participation formats in AI governance must evolve from static consultation to structured interaction. One promising approach is the use of "decision labs" or pilot environments, where participants work on real or simulated scenarios and structure their reasoning collectively before reaching conclusions. This allows ideas to be tested under conditions of uncertainty, rather than discussed in abstraction. Another approach is to introduce guided frameworks that help participants organize their thinking during discussions. For example, structuring contributions around what is known, unknown, and interpreted can significantly improve clarity and reduce misunderstandings across disciplines and cultures. Additionally, iterative formats—where inputs are refined over time through feedback loops—can create a more dynamic and adaptive dialogue, aligned with the evolving nature of AI systems. The goal is not to increase participation in quantity, but to improve its quality and impact. Engagement becomes meaningful when participants do not only share opinions, but structure their reasoning together.

One emerging approach to effective AI governance is the development of methodologies that focus not only on regulating systems, but on improving how humans engage with them. In practice, several pilot initiatives and field-based experiences are beginning to explore how structured reasoning can enhance the use of AI, particularly in contexts of uncertainty. These approaches emphasize the importance of making explicit what is known, what is uncertain, and what is interpreted before interacting with AI systems. This perspective introduces a complementary dimension to current governance frameworks. While most policies focus on outputs, compliance, and system behavior, these methodologies focus on the quality of the input that shapes those outputs. For example, in applied contexts such as agriculture or local decision-making environments, structuring empirical observations and acknowledging uncertainty has proven to improve the relevance and reliability of AI-assisted decisions. This is particularly important in settings where data is incomplete and intuition plays a significant role. Another promising practice is the use of AI as a "cognitive mirror," where the objective is not only to generate answers, but to clarify and structure human reasoning. This shifts AI from a tool of automation toward a tool of reflection and alignment. Such approaches remain at an early stage but offer a concrete pathway to complement existing governance efforts by strengthening the human layer of AI interaction. Effective AI governance does not only depend on better systems, but on better structured thinking before using them.