The starting point of the analysis is the fragmented state of AI governance itself. Scholars describe the current landscape as underdeveloped, unorganized and immature, populated by three broad categories of actors. Private sector giants such as Google, OpenAI, Microsoft, IBM and Meta have produced industry guidelines, ethics documents and voluntary commitments, often favoring self-regulation that does not slow the pace of development. Public sector actors, including national governments and intergovernmental bodies like the G20, the Organisation for Economic Co-operation and Development and the United Nations, have published national strategies and international agreements. Non-governmental organizations, professional bodies such as the IEEE, think tanks and advocacy groups round out the picture, setting standards and drawing attention to emerging risks. Yet despite this flurry of activity, there is still no generally recognized global governance mechanism for AI, and no consensus on what one should even look like.

The central obstacle, according to the study, lies in the divergent approaches of the three actors that lead AI development worldwide. The United States has published more AI strategies and policy reports than any other country and pursues a market-driven, less restrictive model, prioritizing sector-specific governance, granting autonomy to federal agencies and allowing industry to shape policy through consultation and voluntary commitments. There is no federal AI law; the most prominent federal action has been the Executive Order on Safe, Secure, and Trustworthy AI, while state legislatures pass a patchwork of regulations on applications ranging from elections to healthcare. China, by contrast, treats AI as an extension of its centrally planned political system, aligning development with national and socialist values through a rule-based approach while tolerating decentralized innovation at the local level. Its 2017 New Generation Artificial Intelligence Development Plan declares the ambition to become the world leader in AI by 2030. The European Union has carved out a third position, weaving the narrative of trustworthy, human-centered AI into global discourse and releasing the EU AI Act, the world’s first comprehensive AI law, which classifies systems into four risk categories with escalating regulatory requirements.

These three actors each want to lead globally, and their competing ambitions generate concrete barriers to cooperation. The EU’s risk-based regulation is perceived by the US as innovation-stifling, while the US preference for self-regulation clashes with both the EU’s legal approach and China’s state-led model. Geopolitics deepens the divide: decoupling technology supply chains, the ongoing chip war and trade tensions have made faster AI innovation a matter of national security for Washington and Beijing, while the EU pursues open strategic autonomy. Ideological differences compound the problem. The US and EU favor collaboration with democratic partners, whereas China’s use of AI for mass surveillance and social scoring has alienated Western states, pushing it toward partnerships with Russia and developing countries through the Belt and Road Initiative and an AI study group within BRICS. Even the preferred modes of cooperation differ, with the US and EU working through existing institutions while China builds new alternative ones, a dynamic that risks accelerating bloc formation in an already fragmented landscape.

The consequences of this non-cooperation are far from abstract. The study catalogs a series of risks that are amplified or directly generated by the standoff between the three powers. Heightened nationalism in AI development could divert attention from global concerns such as sustainability toward short-term economic interests, fostering protectionist policies that disrupt markets and widen inequalities between nations. The US-China rivalry could spill over into other international conflicts as more countries pursue AI purely for competitive advantage, concentrating power among leading nations while dispersing costs widely. A hollowed-out multilateral system could fragment into minilateral groupings, enabling forum shopping in which countries align with the least restrictive regime. Cross-border risks, from AI-driven cyberattacks to nefarious uses of open-source models, become harder to manage without joint research and coordination, and societal-scale risks from frontier AI models could go unassessed without an international consortium pooling expertise and regulatory resources.

Perhaps most alarming is the prospect of a race to the bottom, in which each actor prioritizes speed over safety, cutting corners to be first to achieve breakthroughs. Coordination would help establish AI safety standards and agree on red lines that should not be crossed, including military applications such as lethal autonomous weapons systems and bioweapons. A competitive race toward artificial general intelligence among the three most capable actors could expose humanity to existential and global catastrophic risks, the study warns, making collective management of long-term dangers as urgent as addressing immediate harms.

To diagnose the problem, Olugbade turns to the collective action literature, a field transformed by Mancur Olson’s 1965 book The Logic of Collective Action and later by the Nobel laureate Elinor Ostrom. Collective action problems arise when group members pursue short-term individual interests at the expense of long-term shared interests, leaving everyone worse off than if they had cooperated. The key to classifying such problems lies in the nature of the goods at stake, defined by two properties: excludability, whether access can be limited, and rivalry, whether use by one diminishes availability for others. Global AI governance, the study argues, is non-excludable, since any of the three actors can currently influence AI’s direction, but rivalrous, because dominance by one precludes equivalent influence by the others. That combination makes global AI governance a common good, placing it in the same analytical category as the common-pool resources Ostrom famously studied, from fisheries to irrigation systems.

Ostrom distilled eight design principles from long-enduring institutions that successfully managed common-pool resources, and the study applies each to AI governance. Clearly defined boundaries would mean identifying who has a right to do what, using multistakeholder mechanisms while granting the three leading powers special privileges of influence as incentives to participate. Rules must be congruent with local conditions, since centrally determined universal rules are unlikely to survive the ideological differences among the three actors; regional and local mechanisms must complement global ones. Collective-choice arrangements should allow stakeholders at every level to modify rules as the technology evolves. Monitoring and graduated sanctions are deemed crucial, addressing the enforcement weakness that has undermined initiatives like the UNESCO Recommendation on the Ethics of AI and the OECD AI Principles. Mutual monitoring between the US and China, each interested in the other’s activities, could be leveraged, with sanctions escalating progressively based on compliance history. Conflict-resolution mechanisms, minimal recognition of the rights of governance bodies to organize, and nested enterprises complete the framework.

The study also draws on the analytical framework for large-scale collective action developed by Sverker Jagers and colleagues, which identifies facilitators such as trust, reciprocity and reputational stake, and stressors such as anonymity, lack of accountability, heterogeneity, risk and uncertainty. Applied to AI governance, the facilitators include a shared perspective among all three actors on using AI to solve global challenges and a common willingness to work with international organizations, with China engaging the UN and the US and EU supporting the OECD. These glimmers of common ground, the study suggests, could serve as entry points around which global AI governance is organized, with international organizations such as the UN, the OECD and the Global Partnership on AI acting as third-party interventions with the capacity and legitimacy to generate cooperation, including through the newly proposed International Scientific Panel on AI.

The study’s central conclusion challenges the many proposals for a single centralized global AI authority, such as a G20 coordinating committee, an International Artificial Intelligence Organization or an Intergovernmental Panel on AI modeled on the climate body. Such centralized mechanisms, the analysis finds, are less likely to succeed than a polycentric, multilevel arrangement in which interconnected but independent governance institutions operate at global, regional, national and subnational scales, each determined by the stakeholders at that level and guided by the principle of subsidiarity. Polycentric systems allow learning from experimentation, build trust, respond to local contexts and reduce opportunistic behavior. Enforcement, supported by monitoring, sanctioning, information provision and conflict resolution, remains the linchpin of implementation. The author acknowledges that the paper focuses on institutional design considerations rather than specifying exact institutions, and calls for future research on how polycentric arrangements could be implemented and how information provision should be structured. But the message is clear: any global AI governance proposal that ignores the non-cooperation of the United States, China and the European Union is likely doomed to fail, and the path forward runs not through a single world AI authority but through a resilient mosaic of overlapping, mutually adjusting institutions.

Subject of Research: Global governance of artificial intelligence analyzed through collective action theory

Article Title: In search of a global governance mechanism for Artificial Intelligence (AI): a collective action perspective

Article References: Olugbade, O. (2025). In search of a global governance mechanism for Artificial Intelligence (AI): a collective action perspective. Global Public Policy and Governance, 5(2), 139-161. https://doi.org/10.1007/s43508-025-00113-z

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00113-z

Keywords: artificial intelligence, global governance, collective action, common-pool resources, Elinor Ostrom, polycentric governance, EU AI Act, US-China rivalry, institutional design, AI regulation, international organizations, frontier AI risks

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

The research, led by Maria Sylvia Macchione Saes and colleagues at the University of São Paulo together with partners at the Federal University of Pará and the Federal University of Western Pará, frames its question through the theory of sociotechnical transitions. In that framework, radical innovations emerge in protected niches, while the dominant regime—the entrenched rules, practices, and market structures of conventional production—resists change until pressures from the broader landscape, such as climate agreements and traceability demands, open windows of opportunity. The authors argue that the conventional bioeconomy model exported from the Global North, focused on industrial biotechnology and biofuels, fails to address the realities of the Amazon, where millions of people depend directly on standing forests for their livelihoods. Their alternative concept, the sociobioeconomy, couples biodiversity conservation with social inclusion and the valorization of traditional knowledge, with local communities as protagonists rather than raw material suppliers.

The pirarucu case offers the study’s most striking quantitative evidence. By the end of the twentieth century, unregulated capture had driven stocks of this iconic freshwater fish toward collapse and onto endangered species lists. The turning point came through a sequence of Brazilian environmental agency rulings that recognized local fishing agreements, exempted managed areas from blanket closed seasons, and introduced individual tagging of legally caught fish. The technical heart of the system is a counting method developed by researchers at the Mamirauá Institute for Sustainable Development working alongside fishers, who identify pirarucu populations by observing the characteristic surfacing behavior of the animals. Those counts determine the annual harvest quota authorized by the federal environmental agency IBAMA, capped at thirty percent of the adult stock above one and a half meters. Each fish landed receives a tag recording its origin, size, sex, and date, creating traceability along the entire chain.

What makes the arrangement scientifically interesting, the authors argue, is its governance architecture. Drawing on Elinor Ostrom’s principles of polycentric governance, they show how multiple decision-making centers—community associations, fishermen’s colonies, NGOs, and federal, state, and municipal agencies—coordinate without any single authority dominating. Annual community assemblies set quotas and investment priorities, while voluntary environmental agents from the communities monitor lakes during the critical December-to-June period, compensating for the structural limits of state enforcement in remote territories. Because the quota a community receives is proportional to the stock it protects, conservation becomes an economic self-interest rather than an externally imposed sacrifice. An enforcement officer from the Chico Mendes Institute for Biodiversity Conservation reported that early enforcement trips once seized more than a ton of illegal pirarucu each, while the last three trips yielded zero kilos, since legal management earns fishers roughly three times more than clandestine sales.

The ecological results are dramatic. Since the first management initiative began in 1999, pirarucu stocks in managed lakes have increased by approximately 620 percent, while unmanaged areas continued to decline. The practice has spread to 48 fishing agreements across 25 municipalities, benefiting around 415 communities and more than 8,000 families, and a federal payment-for-environmental-services program for community-based pirarucu management was recently approved. Monitoring remains expensive—roughly thirty percent of total production cost—and illegal fishing persists, but the researchers describe the model as a replicable social technology that has begun to reshape the institutional regime itself.

The second case examines fine cocoa in the Transamazônica region of Pará, where cocoa cultivation dates to government colonization schemes of the 1970s. For decades, farmers sold bulk beans to intermediaries, with profits captured outside the region and a small group of companies controlling around ninety percent of Brazil’s cocoa bean market. From the 2000s onward, rising global demand for premium chocolate, traceability requirements, and state programs supporting agroforestry systems created space for a niche of cooperatives producing fine-flavor cocoa and bean-to-bar chocolate. Cooperatives such as COOPATRANS, owner of the Cacauway brand, disseminated controlled fermentation and standardized drying techniques, while universities and research institutions built local processing capacity. The result is a partial reconfiguration of the value chain: producers have internalized processing stages, won national and international quality awards, and diversified into their own shops and product lines, reducing historical dependence on middlemen. Agroforestry cultivation has simultaneously helped regenerate degraded land and relieve pressure on remaining forest.

The third and fourth cases operate transversally across value chains rather than within a single commodity. In the Lower Amazon, artisans who craft cuias—vessels made from the fruit of the cuieira tree, a tradition practiced by Indigenous and riverside communities for at least three centuries—organized into the association Asarisan and in 2014 registered the Aíra collective brand, the first of its kind in Pará. After IPHAN recognized the craft as Brazilian Cultural Heritage in 2015, the brand allowed artisans to bypass middlemen entirely: while intermediaries paid the equivalent of about US$0.29 per painted cuia, prices on the brand’s own website range from US$1.56 to US$3.92, at least five times higher. Yet the case also exposes the limits of polycentricity when power is uneven. The artisans depend heavily on institutional partners for bureaucratic brand management, and the authors note that such hidden asymmetries constrain the community’s capacity for joint value creation even when a product’s symbolic value is high.

Origens Brasil, orchestrated by the NGO Imaflora, is the largest experiment of the four: a network of 206 stakeholders, including 39 companies, 79 communities, and 88 NGOs, operating across 62 million hectares of conservation areas. The platform enforces ethical trade rules—transparent negotiation, fair prices, respect for traditional ways of life, and formalized contracts—while deliberately refusing to mediate negotiations, which occur directly between producers and companies. Between 2016 and 2025, the network grew from 3 to 79 communities, from 5 to 43 companies, and from roughly R$520,000 to more than R$8.3 million in annual transactions. Community members report returning youth, near-parity between men and women in income-generating activities, and a growing sense that buyers are paying not just for products but for conservation history itself.

Across all four cases, the authors identify a common structural shift: from a monocentric regime dominated by intermediaries who centralize information and capture value, toward networked polycentric arrangements in which decision-making is distributed among communities, NGOs, cooperatives, and state agencies that coordinate, monitor one another, and co-create rules. Orchestrators such as Imaflora and the Mamirauá Institute function as connective nodes rather than hierarchical authorities. The barriers that remain are sobering—high logistics and surveillance costs, difficult certifications, pervasive informality, and institutional fragility—but the study’s central conclusion is that successful transitions depend less on the specific product than on the robustness of governance and the equity of value distribution. For the Amazon, the researchers suggest, saving the forest may ultimately be an institutional design problem, solved not by new technologies alone but by reconfiguring who holds power over the value that forests generate.

Subject of Research: Governance and institutional drivers of sociobioeconomy transitions in the Brazilian Amazon

Article Title: Transitions to Sociobioeconomy in the Amazon: Governance, Business, and Sustainable Innovations

Article References: Transitions to Sociobioeconomy in the Amazon: Governance, Business, and Sustainable Innovations. (n.d.). https://doi.org/10.1007/s00267-026-02532-y

Image Credits: AI Generated

DOI: 10.1007/s00267-026-02532-y

Keywords: Amazon, sociobioeconomy, polycentric governance, pirarucu, fine cocoa, collective brands, ethical trade, sustainable transitions, traditional knowledge, value chains, conservation, Environmental Management

The study’s central conceptual move is a distinction that sounds simple but carries enormous consequences: the difference between the governance of AI and governance by AI. The first phrase describes the familiar task of regulating, auditing and ethically constraining algorithmic systems. The second describes something far stranger, in which AI systems themselves increasingly supply the inputs, and sometimes the outputs, of decisions that shape public life. As AI shifts from an object of regulation into an active participant in administration, accountability becomes shared between human officials and algorithmic actors, and oversight mechanisms designed for purely human bureaucracies begin to strain. Kaiser argues that this shift turns governance into a hybrid human-machine decision environment, demanding new institutional arrangements rather than incremental policy patches.

The urgency is not hypothetical. The United Arab Emirates has announced plans to use AI to support drafting, reviewing and amending legislation, while Albania introduced Diella, an AI-generated digital minister, as part of procurement and anti-corruption reform. These early, largely political experiments signal a growing willingness to invite AI into decision-relevant spaces once reserved for human officials. Meanwhile, AI policy initiatives now span more than 80 countries and jurisdictions, and technologically advanced states are making billion-dollar investments to secure strategic leadership. The question, the paper suggests, is no longer whether AI enters governance, but whether governance can absorb AI without losing accountability, legitimacy and public trust.

To ground the analysis, the study synthesizes cross-sector risks that recur wherever AI is deployed. Algorithmic bias tops the list: systems trained on non-representative datasets can reinforce existing disparities, as when diagnostic algorithms underperform in minority populations. Real-world failures illustrate the stakes. A wrongful arrest in Tennessee was linked to an AI facial recognition error, and Michigan’s MiDAS system falsely accused thousands of citizens of unemployment fraud through automated determinations. Beyond bias, the review identifies weak oversight, regulatory gaps and capture, vendor dependence, geopolitical and socio-political risks, environmental burdens from energy-hungry data centers, cybersecurity vulnerabilities, and persistent legitimacy and trust deficits. Because a single incident can trigger several of these concerns at once, the author treats them as systemic and interconnected rather than isolated technical problems.

Vendor dependence receives particular attention as an emerging political-economy risk. Procurement arrangements often give public agencies limited access to source code, model documentation, training data and audit trails, weakening data portability and institutional autonomy. Over time, functions that should remain under public authority can become dependent on a small number of dominant technology firms, shifting power, expertise and infrastructure ownership toward the private sector. The paper also flags normative drift, in which the ethical principles embedded in AI systems subtly shift over time without detection, and accountability erosion in multi-stakeholder settings where responsibility is so dispersed that no one can be held answerable when systems fail.

On the theoretical side, the framework integrates three traditions: digital era governance, which explains the movement toward data-driven, reintegrated public services; polycentric governance, drawn from Elinor Ostrom’s work on multiple overlapping centers of authority; and adaptive governance, which emphasizes adjustment under uncertainty. The author extends digital era governance for the AI era by treating hybrid human-AI judgment as an institutional design issue rather than a technical choice. Evidence cited from recent research suggests that human-AI ensembles can improve decision-making and reduce bias, but only when humans retain decision authority and AI inputs remain explainable, reviewable and contestable, supported by audit trails, bias checks, human override and appeal mechanisms.

The practical core of the paper is a six-step adaptive, ethical and responsible AI governance framework that embeds risk management and ethics-by-design across the AI lifecycle. It begins with policy coordination, aligning goals across agencies, levels of government and stakeholders to prevent fragmented rulemaking. Resource allocation follows, building the technical infrastructure, civil-service expertise and independent testing capacity that many public agencies currently lack. Implementation then deploys AI systems with appropriate human oversight and sector-specific standards, such as clinical validation and bias testing in healthcare. Monitoring and evaluation institutionalizes audits, transparency reports and impact assessments, while feedback and learning introduces regulatory sandboxes, periodic reviews and participatory mechanisms that let governance evolve with the technology. The cycle closes with policy adjustment, recalibrating legal and organizational arrangements, including procurement rules, to rebalance public-private power.

To show how the framework works in practice, the study applies it illustratively to two very different contexts: New York City as a decentralized smart city and Singapore as a centralized smart nation. New York has built coordination through its Office of Technology and Innovation, an AI Action Plan, and the earlier Automated Decision Systems Task Force, and in late 2025 the City Council passed the GUARD Act establishing an independent Office of Algorithmic Accountability. Singapore coordinates through its Smart Nation structures, the National AI Strategy 2.0 and the Model AI Governance Framework, supported by GovTech’s shared infrastructure. The comparison reveals a striking trade-off: Singapore’s centralized architecture achieves coherence and rapid strategy updates but offers fewer channels for direct citizen participation and independent redress, while New York’s fragmented system produces uneven capacity but denser external accountability through legislation, civil society advocacy and open data. The framework, the author stresses, is structure-agnostic; it helps each model locate its own strengths and gaps.

The paper is candid about its limits. The framework is conceptual, derived from an integrative literature review rather than primary empirical research, and the city applications are illustrative rather than evaluative. Jurisdictional variability, bureaucratic inertia and uneven resources mean it requires context-sensitive adaptation, and poorly managed compliance regimes could create procedural burden without improving substantive accountability. Still, the concluding message lands with force: governance arrangements themselves can generate new risks over time, so AI oversight must remain adaptive rather than fixed. Even highly capable systems, including agentic AI, remain forms of narrow intelligence operating within bounded goals, but they are already reshaping decision pathways, and preparing for hypothetical artificial general intelligence should be treated as a stress test for existing institutions. The window for establishing robust guardrails, Kaiser argues, is not a future concern but an immediate one, and the health of democratic governance in smart cities and nations may depend on whether institutions learn as fast as the technologies they are trying to steer.

Subject of Research: Adaptive, ethical and responsible governance frameworks for AI in smart cities and nations

Article Title: Adaptive, ethical and responsible AI governance for smart cities and nations

Article References: Kaiser, Z. R. M. A. (2026). Adaptive, ethical and responsible AI governance for smart cities and nations. Discover Cities, 3(1), Article 172. https://doi.org/10.1007/s44327-026-00349-2

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00349-2

Keywords: AI governance, smart cities, agentic AI, algorithmic bias, digital era governance, polycentric governance, vendor dependence, public trust, ethics-by-design, New York City, Singapore, adaptive governance