The distinction the authors draw between AI and earlier waves of digital government is technical as well as institutional. Previous generations of e-government technology primarily improved the efficiency, connectivity, and accessibility of administrative processes, digitising forms and speeding up workflows while leaving the underlying logic of human decision-making intact. AI, by contrast, increasingly performs the knowledge work of administration: it predicts, classifies, optimises, and generates scenario analyses that feed directly into judgements about rights, resources, and sanctions. The central analytical question, the review argues, is therefore no longer how governments digitalise administrative processes, but how administrative intelligence is governed and distributed across human and computational actors. In the authors’ framing, AI is best understood not as a discrete application but as a systems phenomenon whose consequences ripple across power structures, public values, institutions, decision-making, organisations, and frontline bureaucracy.

That systems perspective, drawing on the tradition of systems thinking associated with Barry Richmond and Donella Meadows, rejects the idea that AI’s effects can be assessed by examining any single agency or decision point in isolation. An algorithm deployed in one part of government depends on professional norms, institutional rules, data infrastructures, and frontline judgements that translate algorithmic outputs into real-world action. To capture these interdependencies, the editorial proposes a six-dimension framework covering the full administrative life cycle of AI. The framework moves from the strategic conditions under which governments acquire AI capacity, through the public values used to justify adoption, the institutions that authorise its use, the decisions officials make with it, and the organisations that implement it, to the street-level encounters where algorithms meet citizens. Each dimension, the authors insist, is a location where AI enters, circulates, and becomes institutionalised in government.

The first dimension, power structures, may be the most politically charged. The review argues that public administration scholarship has too often examined AI only after it arrives inside government, ignoring the political economy of the systems being procured. Governments frequently do not own the capabilities their AI depends on: digital infrastructure, specialist talent, computational resources, and research pipelines are concentrated in large technology firms. Drawing on scholarship on platform capitalism and technological sovereignty, the authors describe a fundamental asymmetry in technical capacity, epistemic authority, and regulatory knowledge between states and major tech companies. This dependence shapes what AI systems are available to governments, what officials can know about risk, bias, and explainability, and how much bargaining power agencies have when seeking access to proprietary source code or audit evidence. Supporting research in the special issue, using OpenAlex bibliometric data, finds that Big Tech’s presence in critical technology research is growing, particularly in AI, raising questions about who controls the knowledge on which public-sector AI increasingly rests.

Public value forms the second and normative core of the framework. The authors stress that in government, unlike the commercial sector, AI adoption cannot be justified by productivity or cost reduction alone; it must serve public purposes such as fairness, transparency, responsiveness, and trust. An algorithm that improves technical accuracy does not settle questions of proportionality or legitimacy, because public agencies must justify how decisions are reached and whose interests are served. A service that reduces processing time but increases exclusion or distrust cannot simply be counted as administrative improvement. Contributing papers in the collection develop this argument in detail: one proposes a value-chain approach for tracking where public value is created or lost across the stages of an AI-enabled service, while another maps 3,268 studies using BERTopic modelling and finds that after 2022, work on algorithmic fairness, ethics, and state-society relations has overtaken earlier emphases on efficiency and service delivery.

Institutions constitute the third dimension, translating public purposes into enforceable rules about who may use AI, under what conditions, and subject to which review. The editorial highlights a structural puzzle: most regulatory sandboxes were designed for private-sector innovation, where regulators supervise firms testing products, but in public-sector AI the state is simultaneously regulator, user, implementer, and accountable authority. One contributing paper adapts the sandbox model into an experimentalist governance framework for government AI, emphasising supervised testing, stakeholder participation, iterative learning, and institutional feedback. The authors connect this to Elinor Ostrom’s nested levels of institutional rules, showing how operational rules govern day-to-day AI use, collective-choice rules govern how those rules are revised, and constitutional-choice rules keep experimentation consistent with administrative law, procurement requirements, and rights protection. Institutions, in this account, do not merely permit or prohibit AI; they define the terms under which algorithmic systems can be tested, trusted, and treated as legitimate parts of public administration.

The framework’s fourth dimension turns to administrative decisions, where the editorial examines experimental evidence on whether public officials actually accept AI-assisted judgement, particularly in budgeting, a domain long understood as political rather than purely technical. The review contrasts two views of human-AI collaboration: augmentation, in which algorithms provide forecasts and rankings without displacing human responsibility, and de facto automation, in which officials defer to algorithmic outputs they lack the expertise or organisational backing to challenge. Extending Nathan Caplan’s two-communities theory, the authors suggest that technical experts, vendors, and data scientists may produce algorithmic knowledge that bureaucrats do not regard as usable or congruent with their problems, producing a decoupling between formal AI adoption and practical utilisation. Bureaucratic acceptance, they argue, is an overlooked condition for successful AI reform: officials must come to see algorithmic inputs as credible, legitimate, and compatible with the responsibilities attached to public office.

Organisations and street-level bureaucrats complete the framework. Implementation, the editorial stresses, is rarely smooth: public agencies inherit legacy systems, fragmented data arrangements, risk-averse cultures, and budget constraints, so AI reforms may look successful at launch yet lose momentum as maintenance costs rise and staff revert to familiar practices. Contributing research on AI-powered platform government shows that organisational inertia is not mere resistance but a mechanism through which prior investments and routines govern the pace and direction of AI use. At the frontline, drawing on Michael Lipsky’s theory of street-level bureaucracy, the authors argue that AI does not eliminate discretion but redistributes its materials, changing what officials see, how cases are categorised, and how departures from system recommendations must be justified. Ethnographic work in Finnish public organisations introduces the concept of the moral crumple zone: bureaucrats become accountability buffers who absorb blame for algorithmic failures while lacking meaningful control over the systems that shape their decisions. Experimental evidence from policing further finds that AI-supported enforcement can shift officers toward more formal, coercive styles, depending on the accountability mechanisms in place.

The editorial’s conclusion is stark in its ambition: AI represents a genuine paradigm shift rather than the latest phase of digital government, because it begins to change the nature of administration itself. If digital government changed the machinery of administration, the authors write, AI is displacing the human monopoly over administrative judgement. The review closes with a forward research agenda calling for scholarship to move from national AI policy to AI ecosystems, from AI ethics to AI legitimacy, from adoption to institutional capability, and from street-level discretion to behavioural adaptation. The decisive question for democratic governance, the authors argue, is no longer how governments digitalise administrative processes, but how they govern administrative systems in which computational actors increasingly participate alongside human ones, and whether citizens can still recognise, understand, and challenge the decisions that shape their lives.

Subject of Research: A systems framework for understanding how artificial intelligence enters, circulates, and becomes institutionalised across the public administration system.

Article Title: Artificial intelligence across the public administration system: public values, institutions, and bureaucrats in the algorithmic age

Article References: Cho, W., Fan, Z., & Zheng, Y. (2026). Artificial intelligence across the public administration system: public values, institutions, and bureaucrats in the algorithmic age. Global Public Policy and Governance, 6(2), 103-123. https://doi.org/10.1007/s43508-026-00151-1

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00151-1

Keywords: artificial intelligence, public administration, public value, bureaucracy, street-level bureaucracy, algorithmic governance, technological sovereignty, regulatory sandboxes, digital government, administrative discretion, AI accountability, public sector innovation

The study, published in the journal Global Public Policy and Governance, was conducted by Shaleen Khanal of the Centre for Trusted Internet and Community at the National University of Singapore and Hongzhou Zhang of the S. Rajaratnam School of International Studies at Nanyang Technological University. Motivated by the theoretical framing of power-knowledge, the idea, associated with Michel Foucault, that the production of knowledge and the exercise of power are deeply intertwined, the authors set out to measure whether the concentration of innovation inputs in corporate hands is translating into a concentration of scientific output and agenda-setting power. The stakes, they argue, extend far beyond academic sociology: whoever controls the production of knowledge in critical technologies ultimately shapes the direction, safety and governance of technologies that will define economic and geopolitical power for decades.

To move beyond anecdote, the researchers assembled an unusually large evidence base. Using OpenAlex, an open-access index of scholarly works, they identified 3.28 million publications on critical and emerging technologies published between 2001 and 2024, spanning fields such as artificial intelligence, quantum computing and biotechnology. They then documented the share of these outputs authored or co-authored by scientists affiliated with large technology firms, using author-level affiliation data rather than publication-level labels alone, a methodological choice that allowed them to capture researchers who move between universities and industry. Within this corpus, they drilled down into artificial intelligence, the technology where concerns about corporate capture have been loudest, to assess both the extent and the character of Big Tech’s influence.

The headline result is mixed evidence. Across the full landscape of critical and emerging technologies, Big Tech’s footprint is significant but far from dominant, contradicting the strongest versions of the epistemic capture thesis. Public research institutions remain central to knowledge production in most critical fields, and corporate publishing varies widely across domains, reflecting the different economics of basic and applied research in each area. In artificial intelligence, however, the picture is different: the study documents an unmistakable and increasing presence of Big Tech-affiliated scientists over the two decades covered by the data, consistent with a broader literature showing that industry’s share of leading AI papers has climbed steeply since the deep learning revolution made massive datasets and computing clusters decisive inputs.

That escalation is unsurprising given the underlying resource asymmetry. Training frontier AI models requires computing infrastructure, specialized hardware and capital budgets that few universities can match. Previous scholarship has described a compute divide in which academic labs are progressively squeezed out of state-of-the-art AI research, and high-profile controversies, including the departure of prominent ethics researchers from major labs, have fed concerns that corporate priorities shape not only what gets researched but what may be published. Yet the new analysis complicates the gloomiest narratives. The authors note that emerging evidence suggests Big Tech’s influence on research has in some respects been overtly exaggerated, and their own data show that corporate-affiliated scientists have not colonized critical technology research wholesale, even as their presence in AI grows year after year.

One of the study’s most striking findings concerns collaboration. Publications co-authored by Big Tech researchers and other knowledge institutions, including universities and public research organizations, are associated with higher research impact than papers produced in isolation. This suggests that the flow of industrial resources, data and engineering expertise into joint projects is not merely extracting value from academia but genuinely raising the scientific quality and visibility of the resulting work. Collaborative papers also show a higher probability of including keywords related to trustworthy AI, indicating that corporate-academic partnerships may, at least in bibliometric terms, be pulling attention toward questions of safety, fairness and accountability rather than away from them. The finding challenges a simple capture story in which industry involvement necessarily narrows the research agenda toward commercially lucrative topics.

At the same time, the authors are careful not to declare the concerns unfounded. The theoretical framework of power-knowledge implies that influence operates through channels that bibliometrics can only partially reveal: the setting of technical standards, the funding of labs, the revolving door of personnel between firms and governments, lobbying networks, and the strategic control of research infrastructures such as cloud computing platforms. Other work by the same research team has documented how Big Tech firms act as super policy entrepreneurs, increasing their power in the policy process around generative AI, and how public-private relations in areas such as defense and security governance are being restructured around corporate technological capabilities. The new bibliometric evidence should therefore be read as calibrating the debate rather than closing it.

The findings carry direct implications for technological sovereignty, a concern that has moved to the center of policy debates in the United States, Europe, China and beyond. Governments increasingly treat critical and emerging technologies as strategic assets, screening foreign investment, restricting exports and funding national research programs precisely because control over knowledge production is understood as control over future power. If a small number of private firms, rather than public institutions, dominate the production of knowledge in these fields, then national strategies aimed at sovereignty must grapple not only with foreign rivals but with domestic corporate concentration. The study’s evidence that Big Tech is most entrenched in AI, arguably the most consequential critical technology, sharpens this dilemma: policymakers who depend on corporate labs for national competitiveness may find that the same dependence erodes public capacity for independent assessment, regulation and basic research.

For universities and research funders, the message is double-edged. The positive association between Big Tech collaboration and research impact creates real incentives to deepen industrial partnerships, and such partnerships have produced celebrated scientific advances, from protein structure prediction to large-scale language models. But the study’s framing also echoes calls from prominent university leaders that academia must reclaim independent capacity in AI research for the public good, ensuring that questions about societal risk, ethics and long-term safety are pursued even when they offer no commercial return. The risk identified in the broader literature is that corporate funding and infrastructure dependency can produce insularity, recency bias and quiet suppression of unfavorable findings, dynamics that citation counts alone will not capture.

Ultimately, the research offers an evidence-based middle position in a debate that has often been driven by rhetoric. Big Tech has not captured the production of scientific knowledge across critical technologies, but its presence in artificial intelligence research is growing steadily and its collaborative ties amplify both its scientific contribution and its agenda-setting influence. The privatization of critical knowledge infrastructure, the authors conclude, remains a live geopolitical risk, one that demands governance responses calibrated to the actual scale and character of corporate power rather than to either panic or complacency. As data, compute and capital continue to concentrate, the question is no longer whether Big Tech matters for frontier science, but how democratic societies can harness its capabilities while preserving the independence of the knowledge systems on which they depend.

Subject of Research: Big Tech's influence on scientific research in critical and emerging technologies such as artificial intelligence

Article Title: Power-knowledge and strategic control: how Big Tech is reshaping research in critical technologies

Article References: Khanal, S., & Zhang, H. (2026). Power-knowledge and strategic control: how Big Tech is reshaping research in critical technologies. Global Public Policy and Governance, 6(2), 199-226. https://doi.org/10.1007/s43508-026-00150-2

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00150-2

Keywords: Big Tech, artificial intelligence, AI research, critical and emerging technologies, power-knowledge, bibliometrics, technological sovereignty, trustworthy AI, epistemic capture, industry-academia collaboration, strategic, control