When Machine Learning Meets Policy: Why AI Strategy Needs Interdisciplinary Thinking

Artificial intelligence is often discussed in extremes: either as a purely technical challenge to be solved by better models, or as a societal risk to be managed through regulation and ethics frameworks. In practice, however, AI lives in the space between these two worlds. Over the past several years, my work has focused on exactly that intersection, where machine learning meets policy, institutions, and governance, and on what we gain when we stop treating them as separate domains.

My research began with a simple but underexplored question: how do governments and institutions actually articulate their priorities, values, and assumptions about AI? National and sub-national AI strategies are not just technical roadmaps. They are narratives. They signal what a country believes AI is for, who it should benefit, what risks matter, and which trade-offs are acceptable. Yet much of the analysis of these strategies has relied on selective reading, predefined categories, or narrow ethical checklists.

To address this gap, I turned to machine learning, not as an end in itself, but as a tool for systematic interpretation. Using unsupervised learning methods such as topic modeling, I treated AI strategy documents as large-scale textual corpora and asked what themes emerge without being imposed in advance. This approach allows us to surface priorities that may not be explicitly labeled as ethics, equity, or innovation, but nonetheless shape how AI is governed in practice.

However, computational methods alone are not enough. Machine learning models can identify patterns, but they do not explain their significance. That is where interdisciplinary collaboration becomes essential. By working closely with policy scholars, social scientists, and domain experts, I focused on making these computational findings interpretable, stable, and meaningful in real governance contexts. In practice, this meant repeatedly reviewing model outputs with policy scholars, who flagged when themes shifted across runs in ways that undermined interpretability, prompting methodological refinements that prioritized stability and reliability. This process led to methodological innovations that emphasize reproducibility, robustness, and human-in-the-loop validation, which are critical requirements when AI is used to study high-stakes policy decisions.

More recently, my work has expanded to examine how AI strategies differ across national, sub-national, and international levels, and how values such as fairness, accountability, and health equity are unevenly emphasized across contexts. These analyses highlight an important lesson. AI governance is not monolithic. It is shaped by institutional capacity, political economy, and local priorities. Understanding this variation requires both technical rigor and contextual insight.

Why does this matter for AI strategy today? Because many of the challenges we face, including trustworthy AI, responsible deployment, and alignment with societal goals, cannot be solved by better models alone, nor by policy in isolation. They require tools that can translate between technical systems and institutional realities.

Interdisciplinary work is not always easy. It requires learning new languages, questioning assumptions, and resisting the temptation to oversimplify. But it is precisely this synthesis that allows us to move from abstract principles to evidence-based, actionable AI strategies. As AI continues to shape economies and governance worldwide, bridging machine learning and policy will be not just useful, but necessary.