AI Governance Is Splintering Worldwide, but a Polycentric Fix Is Emerging

Artificial intelligence is no longer a technology that governments can afford to watch from the sidelines. It is rewriting the rules of defense, policing, public administration, and the relationship between citizens and the state, and it is doing so faster than most regulatory systems were ever designed to handle. A new synthesis published in the journal Global Public Policy and Governance, written by Dwayne Woods of Purdue University, pulls together five studies from a special issue on AI and public policy and arrives at a conclusion that is both sobering and quietly optimistic: the world’s approach to AI governance is deeply fragmented, but a workable model for coordination, known as polycentric governance, may already be taking shape.

The special issue examines AI’s policy implications from five distinct angles. One study probes public trust and opinion within the European Union, another compares governance models in Singapore and France, a third frames global AI governance as a classic collective action problem, a fourth dissects regulatory challenges in the defense sector across the European Union and the United States, and a fifth investigates the ethical and legal implications of facial recognition technology in both democratic and autocratic states. Taken together, they map the fault lines where national sovereignty, ethical oversight, and international cooperation collide, and they suggest that no single country or institution can manage the technology alone.

The comparative analysis of national governance models reveals just how different the world’s responses have become. In a study of Singapore and France, Alibašić shows how international organizations and global governance frameworks shape national AI strategies in markedly different ways. Singapore has built an innovation-centric model, one that treats regulatory agility as a competitive advantage and leans heavily on market-driven experimentation. France, by contrast, has pursued an ethics-driven approach in which the state plays a leading role in setting boundaries and safeguarding public values. The contrast is not merely stylistic. It illustrates a fundamental tension in AI policy worldwide: whether the primary goal of regulation is to accelerate innovation or to constrain it in the name of rights, safety, and democratic accountability.

That tension becomes even sharper when the technology in question is designed for the battlefield. Parisini’s comparative study of the European Union and the United States in the defense sector underscores the fragmented nature of EU defense AI governance, which is spread across institutions and member states, and contrasts it with the far more centralized strategy of the US Department of Defense. The analysis identifies capability gaps within the EU that could be narrowed through stronger transatlantic cooperation, an argument that echoes the findings on Singapore and France about the importance of international coordination. In other words, whether the domain is civilian innovation or military capability, the same lesson emerges: jurisdictions that try to go it alone leave gaps that cooperation could close.

At the global level, the picture grows more daunting still. Olugbade situates these national differences within a broader framework, arguing that AI governance is fundamentally a collective action problem, one made worse by the non-cooperation of the three dominant powers: the United States, China, and the European Union. When the largest producers and deployers of AI cannot agree on shared rules, smaller states are left to choose among incompatible standards, and the technology flows through the cracks. Olugbade’s answer is a polycentric governance mechanism, a system in which multiple centers of authority, from national regulators to international organizations, each govern parts of the problem while reconciling national autonomy with global cooperation. Rather than waiting for a single worldwide treaty that may never arrive, polycentricity accepts overlapping jurisdictions and seeks alignment among them.

The stakes of this fragmentation are not abstract. Robles and colleagues examine regulatory frameworks for facial recognition technology across a range of political regimes and document how unevenly such measures are applied. In democratic states, facial recognition raises acute concerns about civil liberties, wrongful identification, and the chilling of lawful assembly; in autocratic states, the same technology can become an instrument of surveillance and social control. The study’s central finding, the need for harmonized international standards, echoes a sentiment running through the entire special issue: without common benchmarks, the ethical floor for AI technologies will be set by the least restrictive jurisdiction, and the consequences will spill across borders.

Public trust emerges as the connective tissue binding all of these analyses together. Jensen and Chen focus on public sentiment toward AI within the European Union and identify class-based divides in how the technology is perceived, along with skepticism rooted in deficits of governance trust. Citizens who distrust the institutions regulating AI are unlikely to accept its deployment, however technically sound the safeguards may be. The authors propose citizen engagement and transparency initiatives as mechanisms to bridge this trust gap, a micro-level prescription that complements the macro-level analyses of global governance offered elsewhere in the issue. The implication is that legitimacy must be built at both ends of the scale: in the deliberations of international bodies and in the lived experience of ordinary people confronting algorithmic decision-making.

What makes the synthesis compelling is the convergence of these five studies on a shared set of themes: governance fragmentation, public trust, ethical considerations, and international cooperation. The comparative evidence suggests that harmonizing AI governance requires multilevel collaboration, both within regions such as the European Union and across global power blocs that currently view each other with suspicion. The collective warning is stark. Without coordinated global policies, AI governance will remain uneven, and that unevenness will put democratic accountability and international security at risk simultaneously. A technology that respects no borders cannot be governed effectively by institutions that stop at them.

The policy implications are concrete. The synthesis calls for enhanced international cooperation, stronger citizen engagement to foster trust, and the establishment of ethical standards for emerging technologies such as facial recognition. It also sketches institutional mechanisms that could translate high-level policy convergence into practice without demanding complete harmonization. Regulatory sandboxes, flexible spaces where governments can trial data-sharing protocols or algorithmic audits under shared principles while maintaining oversight, offer one path for incremental alignment. International AI observatories, hosted by multilateral organizations such as the OECD, UNESCO, or the UN Tech Envoy, could coordinate monitoring, ethics benchmarking, and risk assessment across jurisdictions. Capacity-building initiatives, including South–South knowledge transfer networks and AI policy toolkits tailored to institutional contexts, would support low- and middle-income countries that currently lack the regulatory infrastructure to engage on equal terms.

The research agenda that follows is equally clear. Future work should explore mechanisms for enforcing global AI standards while respecting national sovereignty, and should conduct empirical studies on whether polycentric governance frameworks actually deliver in practice. Multi-scalar research comparing AI implementation across local, national, and regional levels would reveal where governance gaps emerge and how they might be mitigated. The special issue as a whole advocates a multi-level, polycentric approach that bridges the gap between national autonomy and global regulatory alignment, setting the stage for AI governance that is both more robust and more democratically accountable. The alternative, a world in which each bloc writes its own rules and trusts no one else’s, is not a stable equilibrium but a slow-motion race to the weakest standard. The choice, the synthesis suggests, is still open, but the window for making it deliberately is narrowing with every deployment.

Subject of Research: Comparative analysis of global AI governance frameworks and polycentric policy coordination

Article Title: From fragmentation to polycentricity: a comparative synthesis on AI governance and global policy coordination

Article References: Woods, D. (2025). From fragmentation to polycentricity: a comparative synthesis on AI governance and global policy coordination. Global Public Policy and Governance, 5(2), 87-92. https://doi.org/10.1007/s43508-025-00118-8

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00118-8

Keywords: artificial intelligence, AI governance, polycentric governance, global policy coordination, public trust, facial recognition technology, EU regulation, defense sector, collective action, regulatory sandboxes, international organizations, comparative public policy