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, conducted by Zander Weisman Mintz of the LBJ School of Public Affairs at the University of Texas at Austin and Ji Ma, now of Indiana University Bloomington and the University of Oxford, combines a large-scale systematic literature review with cutting-edge computational text analysis. Drawing on OpenAlex, an open scholarly database, the researchers built a corpus through citation snowballing that began with four foundational review articles and expanded forward through more than 14,900 candidate works. They then screened this mountain of material using Meta’s open-source Llama3 large language model, employing role prompting and chain-of-thought prompting to classify each candidate against six qualifying conditions. Validation against a manually reviewed sample of 200 works achieved 91 percent agreement. The final corpus comprised 3,268 works, published between the mid-1980s and May 2026.

To extract the latent thematic structure of this literature, the team applied BERTopic, a neural topic-modeling technique that leverages transformer-based document embeddings and clusters abstracts into coherent topics represented through class-based term frequency–inverse document frequency. The method outperformed conventional bag-of-words alternatives such as latent Dirichlet allocation and non-negative matrix factorization, particularly on the short texts typical of academic abstracts. Hyperparameters were optimized through random grid search to maximize topic coherence, and outlier reclassification plus manual relabeling refined interpretability. The result was a map of twenty distinct topics spanning the entire field, from healthcare AI and municipal waste optimization to algorithmic fairness and citizen participation in datafied democracies.

But topic modeling alone would simply produce another taxonomy. The study’s central contribution is a functional framework that organizes the literature around four practical functions of public governance: creating public value, delivering public services, responsiveness to the public, and protecting state–society relations. The authors deliberately rejected more familiar organizing axes. Sorting by application domain scatters shared governance problems like bias and accountability across every sector. Sorting by technology tracks capability but ignores the public stakes that make government deployment distinctive. Sorting by governance risk collapses the field into its pathologies. And sorting by public value alone absorbs distinctions a public manager most needs to see. The four governance functions, the authors argue, are the questions a public manager actually asks before deploying a system: does it create value, improve a service, change how the organization hears from citizens, and what does it do to the state’s relationship with the governed?

The framework’s dimensions are enriched by four theoretical lenses that recur throughout the scholarship: technological operationalization, which asks how AI fits specific public problems; function of government, which maps AI techniques onto sectoral activities; the policy-cycle framework, which locates AI within agenda-setting, formulation, implementation, and evaluation; and public governance, which foregrounds who is affected. Notably, the authors show that technological operationalization is not confined to the application dimensions but runs vertically through all four: a model’s opacity, its optimization target, and its hardcoded thresholds are design choices that propagate into accountability deficits and entrenched inequality downstream. Governance failures, they suggest, are often most cheaply addressed upstream at the point of technical design rather than through oversight bolted on after deployment.

The empirical findings reveal a field in dramatic flux. Output grew rapidly through the late 2010s, peaked at 627 works in 2023, and remained elevated through 2025. More striking is the compositional shift. The public value dimension, dominated by domain applications such as energy-efficiency forecasting and air-pollution monitoring, accounted for 50 to 77 percent of works through 2017 but declined to roughly 16 to 22 percent by 2024 and 2025. The state–society relations dimension, encompassing algorithmic fairness, ethics, and regulation, more than doubled its share, from around 12 to 23 percent in the mid-2010s to 52 to 54 percent in 2024 and 2025. The transition concentrates around the public release of ChatGPT in November 2022, which the authors use as a heuristic marker rather than a causal explanation, noting that generative AI diffusion, regulatory momentum such as the EU AI Act, and an ongoing accountability debate all converged in the same period.

The post-2022 pivot is stark in the numbers. Classical domain-application topics shrank from about 19 percent of the pre-ChatGPT corpus to under 5 percent afterward, while several topics essentially became new literatures: ethics and equity in educational AI grew from 2 works to 66, and AI ethics, accountability, and transparency in public service delivery surged from 9 to 291 works, a thirty-one-fold expansion. Semantic analysis of the twenty topics shows that the six state–society topics form a tight, mutually similar cluster, reading as a single coherent research conversation despite spanning multiple domains, while the public value topics spread widely across incompatible technical vocabularies. The framework thus partitions the field asymmetrically, and the authors argue this asymmetry is itself informative for anyone navigating it.

Equally important is where the literature is thin. The responsiveness dimension, concerning where AI can support the policy cycle and democratic feedback, carries only about 462 works across just two topics, making it the smallest dimension despite AI’s obvious relevance to agenda-setting, consultation, and evaluation. Intriguingly, public administration journals are already over-indexing here: responsiveness accounts for about a quarter of works in top-ranked public administration outlets, nearly double its corpus-wide share. The authors sketch four research directions for this space, including how large language models reshaping the information environment of agenda-setting, whether AI-mediated consultation strengthens or hollows the link between citizen voice and government decision, what happens when optimizing tools are embedded in satisficing policymaking processes, and whether AI-driven evaluation genuinely closes the feedback loop or merely produces dashboards.

The framework also doubles as a practical diagnostic for public managers weighing a specific AI system. Each dimension supplies a question and a concrete check: whether the system creates value the public can recognize, whether it improves a service without eroding recourse to a human, whether it widens or narrows the channel through which citizen preferences enter decisions, and whether affected citizens can learn of, understand, and contest its output while responsibility remains attributable. Applied retrospectively to the City of Chicago’s 2016 predictive policing heat list, a system later judged less effective than a simple most-wanted list, the diagnostic surfaces failures on three of four questions, illustrating how design-stage deficits that later evaluations documented could have been identified before deployment.

The study acknowledges its boundaries, including reliance on abstracts rather than full texts, the undercounting of grey literature and government reports, restriction to English-language scholarship, and the possibility of calibration drift in large-language-model screening. Yet within those limits, the contribution is a translation layer: a compact, function-based vocabulary that makes an interdisciplinary literature, previously scattered across engineering, information systems, and public administration, discoverable and comparable across the communities that study it in parallel. As governments worldwide grapple with deploying AI under democratic constraints, the researchers offer not a new theory of public administration but something arguably more useful right now: a shared map of what is known, what is contested, and where the next decade of scholarship and practice most urgently needs to go.

Subject of Research: A computational mapping of artificial intelligence research in the public sector using a functional governance framework and BERTopic modeling

Article Title: Towards artificial intelligence for the public sector: framing and bridging academia and practice

Article References: Mintz, Z. W., & Ma, J. (2026). Towards artificial intelligence for the public sector: framing and bridging academia and practice. Global Public Policy and Governance, 6(2), 124-156. https://doi.org/10.1007/s43508-026-00148-w

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00148-w

Keywords: artificial intelligence, public sector, public governance, algorithmic fairness, BERTopic, systematic literature review, policy cycle, public value, ChatGPT, accountability, e-government, computational social science

The core insight of the study is deceptively simple. Rather than treating AI as a monolithic technology that is either a savior or a menace, the authors break the deployment of AI in government into a series of interlinked stages: data collection, data storage, algorithm training, and data analysis and usage. At each link in this chain, different values are created, different actors derive benefit, and different problems emerge. Crucially, the authors insist that the AI value chain cannot be represented as a simple linear sequence. Because generative AI systems continuously recycle their outputs back into the chain, feeding new data into model calibration in an ongoing learning process, the appropriate mental model is cyclical rather than linear. This cyclical structure, they argue, is what fundamentally distinguishes generative AI from earlier waves of digital government technology, from voice recognition systems to automated decision-support tools, which have been part of the digital government trajectory since the 1950s.

The analysis begins where every AI system begins: with data. The authors identify four distinct categories of data feeding public sector AI. Historical administrative datasets, such as census records, tax files, welfare benefits and health data, are compulsory, regularly updated and broadly accurate, making them reliable building blocks. Voluntarily shared data, from library card registrations to social media activity, is far more uneven. Data surrendered by non-public agents, including banks, insurers and private contractors delivering day-care or social services, introduces yet another set of provenance problems. Finally, governments increasingly procure data collected by private actors, such as location data from telecom providers used for transport demand modelling or pandemic mobility tracing. Each category carries different expectations of quality, and the latter two are far more prone to duplicates, gaps, inconsistencies and outright errors, which propagate silently into downstream algorithmic decisions.

Bias is the shadow that falls across this first stage of the chain. The study catalogues biases of measurement, representation, aggregation and sampling, many rooted in the digital divide itself. Older citizens are frequently absent from digital datasets, yet the authors caution that younger users are not reliably representative either, lacking the skills to be fully digital natives in the way popular narratives suggest. Socio-economic inequalities in device access persist even amid the proliferation of mobile phones, and commercial data collectors systematically ignore less prosperous groups because they are unattractive customers, precisely the populations public policymakers most need to understand. The authors also warn against the seductive aggregation fallacy, the belief that with enough data the numbers will speak for themselves. Data never speaks for itself, they note; someone always formulates the questions, organizes the material and interprets the results, and correlation is not causation. Big data, they argue, must be supplemented with small data and basic social science.

Privacy challenges emerge at the very start of the chain and, according to the study, never let go. Vast quantities of data are routinely collected without informed consent, despite the GDPR providing a global minimum standard. The authors argue that the contemporary privacy paradigm of notice-and-consent, in which individuals manage their own data permissions, is simply unattainable in an AI context given the multitude of sensory devices, data sources and indefinite possibilities for future repurposing. Once collected, data creates risks of breach, re-identification and profiling, a problem long observed with video surveillance and now magnified by the Internet of Things. Anonymization offers false comfort: removing names does little when residency, workplace, age and shopping habits are sufficient to identify an individual.

The storage stage brings its own hazards. Larger data volumes and reliance on remote cloud storage amplify vulnerabilities to tampering, malicious insiders, data loss and large-scale breaches, and the question of how to isolate and protect sensitive personal information within heterogeneous datasets remains one of the hardest problems in the field. The authors also probe the blurred boundary between public and private responsibility. When public service data is stored or processed by social media companies, data brokers, cloud providers or telecoms firms, does the public agency renounce responsibility for future breaches? And can private organizations whose datasets emerged from public service sources claim copyright, commercialize that data and sell it back to the state, a question that intersects uneasily with the global open government data movement?

Training the algorithm exposes what the authors call one of the key challenges of AI in the public sector: transparency. Computer and data scientists retain the privilege of formulating the purpose of any data process, and algorithms inevitably embed subtle institutional and historical biases reflecting the context of their designers. The mushrooming literature on algorithmic bias offers sobering empirical evidence, from racial bias in facial recognition software used in policing, to predictive policing tools that disproportionately target the usual suspects, to risk-of-reoffending assessments that reproduce discriminatory patterns. At the analysis and usage stage, the study highlights the persistent difficulty of combining disparate datasets, a core premise of government AI that frequently exceeds existing data integration technology, complicated by technical, organizational, semantic and legal incompatibilities, along with unresolved intellectual property questions about who owns learning-based datasets built on public information.

Accountability emerges as perhaps the most consequential value at the end of the chain. In manual government processes, decision-makers are expected to explain and justify their decisions within codified chains of command. When generative AI replaces or shields the human decision-maker, the authors ask, why was this decision made, and who is to blame when a machine decides wrongly, particularly where human oversight is weak or absent? Accountability, they stress, means being called to account, something that cannot be delegated to software. Documented failures cluster in social welfare interventions and justice-system profiling, with recurring complaints of opaque reasoning, transferred human bias, and responsibility floating between developers, suppliers, administrative users and political masters. The authors invoke cautionary precedents such as Australia’s Robodebt scheme and the British Post Office Horizon scandal as evidence of what follows when automated systems fail without clear lines of answerability.

The study’s conclusion is neither a rejection of AI nor an endorsement of it. The challenges identified, the authors write, point instead to the need for governance structures and risk assurance frameworks capable of managing AI in the public interest within a highly fragmented space where public and private actors are intimately meshed. They observe that existing regulation, from data protection law to intellectual property rights and impact assessments, leaves significant gaps around responsibility and accountability, and that current policy responses remain embryonic and dominated by soft instruments. Overemphasizing the technical side of generative AI, they warn, distracts from problems embedded in existing structures and processes of government. It took electronic government roughly a decade to abandon utopian rhetoric for a sober discussion of online service delivery, and the authors argue the AI discourse must make the same transition: understanding the technology in its institutional environment, alongside its impacts and consequences, if individual and societal value is genuinely to be realized.

Beyond its diagnostic framework, the study situates the current wave of adoption within a longer history of technological enthusiasm in government, noting that artificial intelligence has become a symbol for a more productive, accurate and resource-efficient public administration much as big data, smart cities and blockchain did before it. The authors point out that there is still no consensus on what actually constitutes AI, and they adopt the OECD definition of a machine-based system that infers from received inputs how to generate predictions, content, recommendations or decisions, with varying levels of autonomy and adaptiveness after deployment.

A notable addition to the familiar narrative is the authors’ discussion of agentic AI, in which systems move beyond generating content toward making multi-step decisions and reasoning autonomously with other agents. This transition, they suggest, raises the stakes for public sector deployment because machine sense-making begins to replace not only acquired skills but interpretive judgment itself. They balance this against expected benefits drawn from the literature, including more accurate and less biased decisions, cost savings, reduced corruption and improved policy analysis, while stressing that past automated failures, such as flawed welfare debt recovery systems, offer predictable lessons.

The article also emphasizes that governance responses to date have relied largely on soft instruments, such as ethical principles, awareness-raising and assurance frameworks, in the expectation that industry will develop technical standards voluntarily. Meanwhile, cloud-based networked practices erode the capacity of national regulators to supervise AI effectively. The authors repeatedly caution that roll-out is deeply context-specific and shaped by norms surrounding public services, warning that the technology should not be divorced from the institutional environments in which it operates. Their proposed value-chain diagnostics, covering data quality, intellectual property, surveillance, privacy and transparency, are intended as tools for forecasting and evaluating generative AI systems more systematically than the prevailing promises-versus-pitfalls debate allows.

Subject of Research: A value-chain analysis of the opportunities and risks of artificial intelligence in public service delivery and policymaking.

Article Title: A value-chain perspective of artificial intelligence in public services

Article References: Löfgren, K., & Webster, C. W. R. (2026). A value-chain perspective of artificial intelligence in public services. Global Public Policy and Governance. https://doi.org/10.1007/s43508-026-00152-0

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00152-0

Keywords: artificial intelligence, public services, value chain, public value, generative AI, algorithmic bias, data privacy, accountability, digital government, data governance, surveillance, transparency