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