The paper introduces what its author calls the 3×2A Framework, a profile-based interpretive approach designed to coordinate the ethical, functional, and institutional questions raised by AI-mediated organizations. The name encodes its structure: three diagnostic dyads, each containing two interdependent axes. The first dyad pairs automation with augmentation, capturing how work is redistributed between machines and people. The second pairs alliance with alignment, describing the relational quality of human–AI cooperation and the degree to which system behavior tracks human values and intentions. The third pairs adaptation with accountability, addressing how institutions learn and change over time and who answers for outcomes when things go wrong.

What distinguishes the framework from existing checklists and compliance tools is its insistence on situatedness. Rather than asking whether an AI system is safe or fair in the abstract, the 3×2A approach asks how a specific configuration of people, models, tasks, and institutions behaves in a specific context. A radiology department using AI-supported screen reading, for example, occupies a very different position on the automation–augmentation axis than a law office deploying automated contract analysis, even if both rely on similar underlying technology. The framework’s output is a six-axis profile, an ordered qualitative description that reveals functional, relational, adaptive, and accountability-related tensions within that configuration.

The epistemology of these profiles receives careful attention, and it is here that the article makes one of its most consequential moves. Ungureanu is explicit that the qualitative profile bands are ordered, evidence-informed interpretive categories, not numerical or cardinal scores. They convey direction and relative position, but they do not support arithmetic. Where numerical scoring is methodologically justified, it requires explicit rubrics, evidence standards, scoring rationales, and clearly stated interpretive limits. The framework also permits a composite synthesis, the 3×2A quotient, but only as a secondary and conditional construct built on an established profile, subject to documented activity–axis judgments, evidential bases, weighting assumptions, aggregation rules, and interpretive limitations.

This epistemic modesty is a deliberate corrective to a familiar failure mode in AI governance: the seduction of the single number. Risk scores, maturity indices, and compliance dashboards promise comparability and ease of communication, but they can obscure the qualitative judgments buried inside them and create false confidence in precision that the underlying evidence cannot support. By insisting that profiles come first and any quotient comes second, the framework attempts to preserve the richness of situated analysis while still allowing structured synthesis when the evidential ground is firm enough to bear it.

To demonstrate the framework in use, the article offers a worked benchmark re-analysis of a publicly documented case: the environmental-monitoring pilot conducted with the Province of Fryslân in the Netherlands, part of a responsible-use-of-AI project carried out with the Rijks ICT Gilde and the Z-Inspection initiative. That project examined a trustworthy AI and fundamental rights assessment of an AI system used in environmental oversight. Ungureanu re-reads the publicly available documentation through the six axes, producing an ordinal qualitative profile of the configuration without numerical scoring or a composite quotient. The exercise illustrates procedure rather than proof; the author is careful to state that the re-analysis does not claim empirical validation of the framework itself.

The Fryslân benchmark is instructive precisely because it shows the framework’s diagnostic texture. On the automation–augmentation dyad, the relevant question is not whether the AI replaced human monitors but how the division of labor shifted and whether the resulting configuration preserved meaningful human judgment. On alliance–alignment, the analysis probes whether the human–AI relationship functioned as genuine cooperation and whether the system’s outputs remained aligned with the values and legal obligations embedded in the assessment. On adaptation–accountability, it asks how the institution absorbed lessons from the pilot and how responsibility for outcomes was distributed among developers, public officials, and external assessors. Each axis generates tensions that a simple compliance verdict would flatten into invisibility.

The broader intellectual context matters here. The article draws on a rich literature spanning human-centered AI, machine behavior, algorithmic legitimacy, and the sociology of sociotechnical systems. It engages the well-documented observation that fairness and ethics principles often fail when abstracted from the concrete systems in which they must operate, and it echoes calls to shift from asking whether AI is good or fair to asking how it shifts power. It also responds to a growing consensus that generative AI in particular demands adaptive governance: rules and review processes that evolve as systems, users, and institutions co-evolve. The 3×2A Framework can be read as an attempt to give that consensus an operational instrument, one that treats governance as an ongoing interpretive practice rather than a one-time certification.

The stakes are considerable. As AI systems become embedded in knowledge production, labor organization, and public decision-making, the question of who is accountable when automated processes fail grows sharper. Research on moral crumple zones has shown how humans positioned near automated systems can absorb legal and moral responsibility for failures they could not realistically prevent. A profile-based approach makes such misalignments visible by mapping, for a given configuration, where control actually resides, where adaptation happens, and where accountability gaps open. In that sense, the framework functions less as a gatekeeping device and more as a mirror, forcing institutions to confront the structure of their own AI-mediated arrangements.

Critics may reasonably ask whether an interpretive, qualitative instrument can carry the weight of governance, particularly in regulatory environments moving toward harmonized rules such as the European Union’s Artificial Intelligence Act. Ungureanu’s answer, embedded in the framework’s design, is that interpretive rigor and regulatory utility are not opposites. The framework does not replace risk management standards or legal compliance; it complements them by providing a structured way to reason about configurations before and after formal requirements are applied. Its profiles are meant to be evidence-sensitive and revisable, updated as systems and institutions change. Whether the approach gains traction among practitioners will depend on whether organizations find that a six-axis portrait of their human–AI arrangements yields insights that dashboards and checklists miss. The article’s wager is that in a world where humans and machines are coevolving faster than governance can adapt, the most valuable tool is not another score but a clearer way of seeing.

Subject of Research: A profile-based interpretive framework for human–AI coevolution and adaptive AI governance

Article Title: The 3×2A framework: a profile-based interpretive approach to human–AI coevolution and adaptive governance

Article References: Ungureanu, V. (2026). The 3×2A framework: a profile-based interpretive approach to human–AI coevolution and adaptive governance. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03352-8

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03352-8

Keywords: generative AI, large language models, human–AI coevolution, adaptive governance, AI accountability, automation, augmentation, alignment, sociotechnical systems, AI ethics, AI & Society, 3×2A framework

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