Generative AI’s Triple Identity in Government Creates a Governance Impossible Triangle

Generative artificial intelligence has moved from the margins of public administration to its center. It drafts responses to citizens, summarizes case files, forecasts service demand, and increasingly participates in decisions that were once the exclusive preserve of human officials. A new study published in Humanities and Social Sciences Communications argues that this transformation cannot be understood by treating the technology as just one thing. Governments, the researchers contend, are simultaneously deploying generative AI as a tool, regulating it as an object, and in some settings accepting it as a decision-making agent, and the collision of these three identities produces governance dilemmas that existing frameworks were never designed to handle.

The research, conducted by Sihan Lu, Yan Zhou, and Tao Hong of the Harbin Institute of Technology in China, starts from a theoretical observation about the academic literature. Most scholarship on generative AI in the public sector has examined the technology through a siloed lens, focusing on a single role at a time. Studies of AI as a governance tool emphasize efficiency gains and service improvement. Studies of AI as a regulatory object emphasize risk, liability, and the need for oversight. A smaller body of work treats AI as a novel decision-making agent with its own capacity to act. Each perspective is useful, but each is incomplete, and the fragmentation obscures what happens at the micro level when these roles intersect inside a single administrative system.

To overcome that limitation, the authors reconceptualize the public management system as a complex adaptive system, a framework borrowed from the study of ecosystems, markets, and other environments in which many interacting agents produce outcomes no single actor intends. Within this framework, generative AI is modeled as an adaptive agent rather than a static instrument. That shift matters because adaptive agents learn, respond to feedback, and alter the behavior of the other agents around them. A chatbot that answers citizen queries changes how civil servants work; a drafting assistant changes how policy documents are produced; a predictive model changes which cases get attention. The technology does not sit outside the governance system. It becomes part of the system’s own dynamics.

The empirical core of the study is a single-case analysis of Singapore’s Smart Nation initiative, one of the world’s most sustained and comprehensive national programs for embedding digital and intelligent technologies in public administration. By tracking governance strategies and their outcomes across a range of situations within that program, the researchers were able to observe how the same underlying technology was assigned different roles in different contexts, and how those assignments generated tensions that no single regulatory rule could resolve.

The central theoretical contribution of the paper is what the authors call the Governance Impossible Triangle. According to this framework, generative AI’s triple identity creates persistent tensions among three goals that public administrators must pursue at the same time: maximizing efficiency, safeguarding the legitimacy of decisions, and maintaining effective risk control. The triangle is impossible in the same sense that classic policy trilemmas are impossible. Push hard on efficiency, for example by automating more decisions end to end, and legitimacy comes under strain because citizens and officials lose the ability to understand and contest how outcomes were reached. Push hard on legitimacy, for example by requiring extensive human review and explanation, and the efficiency gains that justified adoption begin to erode. Push hard on risk control, and both efficiency and responsiveness suffer. The three corners pull against one another continuously, and no configuration satisfies all of them at once.

What makes the triangle particularly difficult is that the three identities of generative AI activate the three corners in different combinations. As a tool, the technology is primarily an efficiency asset, and the governance question is how much discretion to delegate to it. As a regulatory object, it is primarily a risk to be contained, and the question is how to oversee systems whose behavior even their developers cannot fully predict. As a decision-making agent, it touches legitimacy directly, because democratic governance rests on the premise that consequential decisions are attributable to accountable human institutions. When a single platform shifts between these roles depending on the task at hand, the governance burden shifts with it, often faster than rules and oversight structures can adapt.

The authors’ response to this dilemma is the Contextual Identity Configuration Model, a framework that rejects one-size-fits-all regulation in favor of dynamic, context-sensitive assignment of AI’s role. The model is driven by four variables: problem complexity, risk severity, value conflicts, and explainability requirements. Simple, low-risk, low-conflict problems with clear standards can tolerate a high degree of algorithmic tool-intensity, meaning the AI acts with substantial autonomy as an efficiency instrument. As complexity rises, as stakes rise, as values come into conflict, or as the need for explanation grows, the model calls for recalibrating the system, reducing agentic power and increasing human oversight, so that the technology reverts toward a supportive tool role rather than an autonomous one.

To make this concrete, the study analyzes four prototypical scenarios of AI deployment in public governance. In routine optimization, where tasks are standardized and risks are low, generative AI can operate with high tool-intensity, delivering efficiency with minimal threat to legitimacy. In professional assistance, the AI supports trained experts who retain judgment and accountability, a middle configuration that captures productivity while keeping humans in the loop. In high-stakes discretion, where decisions affect rights, liberties, or significant resources, the model demands strong explainability and human authority, constraining the AI to an advisory function. In complex crises, where speed matters but uncertainty is extreme, the framework calls for layered defenses and continuous feedback so that algorithmic recommendations inform but never replace accountable human command. Across all four scenarios, the governing principle is the same: management systems must dynamically recalibrate algorithmic tool-intensity and agentic power through continuous feedback loops rather than fixing the technology’s role in advance.

The practical implications the authors draw are institutional as much as technical. They argue that governments should build multi-layered defense systems, in which technical safeguards, procedural review, and human accountability operate together rather than as substitutes for one another. They also propose joint accountability networks, arrangements in which responsibility for AI-assisted decisions is distributed across developers, agencies, and oversight bodies in ways that remain legible to the public. The goal, in their formulation, is a profound alignment between technological empowerment and core public values, so that the efficiency of generative AI does not come at the cost of the legitimacy and trust on which public institutions depend.

The study arrives at a moment when many governments are experimenting with generative AI faster than they are theorizing it. Its contribution is to name the structural problem: the same technology that makes administration faster also makes it harder to explain, and the same flexibility that makes it useful makes it hard to regulate. By modeling the public sector as a complex adaptive system and the AI within it as an adaptive agent, the researchers offer policymakers a vocabulary for the trade-offs they are already making, often implicitly. The Governance Impossible Triangle will not be dissolved, the authors suggest, but it can be managed, provided that regulators abandon uniform rules in favor of an agile, context-based governance paradigm in which the identity of the technology, and the controls attached to it, are deliberately configured to match the demands of each task.

Subject of Research: The role and governance of generative AI in public administration and government decision-making

Article Title: The “Triple identity” of generative AI in public governance: dilemmas and adaptive solutions

Article References: The “Triple identity” of generative AI in public governance: dilemmas and adaptive solutions. (n.d.). https://doi.org/10.1038/s41599-026-09400-4

Image Credits: AI Generated

DOI: 10.1038/s41599-026-09400-4

Keywords: generative AI, public governance, complex adaptive systems, Smart Nation Singapore, algorithmic accountability, AI regulation, governance trilemma, decision-making agents, public administration, risk control, legitimacy, adaptive governance