The research, conducted by Wonhyuk Cho of Ewha Womans University in Seoul and Danuvas Sagarik of the National Institute of Development Administration in Bangkok, appears in the journal Global Public Policy and Governance. The authors set out to address a gap that has grown as governments around the world, from Thailand with its digital government development plan to agencies across the European Union, experiment with embedding machine learning in administrative decision-making. While a substantial literature has documented the efficiency gains that AI can deliver in public services, far less is known about how bureaucrats themselves respond when algorithms are proposed for budgetary choices, a domain that concerns not merely productivity but the fundamentally distributive question of who receives funding and who does not.

The institutional viability of any algorithm-enabling budgeting reform, the authors argue, hinges on whether the bureaucrats charged with carrying it out view it as preferable to the alternatives. History offers plenty of cautionary tales on this point. Public administration research has repeatedly shown that bureaucratic organizations resist reforms perceived as threatening, whether those reforms involve shared service centers or broader restructuring programs, and that the success or failure of administrative change often depends on securing cooperation from insiders. If civil servants quietly oppose algorithmic budgeting, even the most technically sophisticated systems could stall in implementation.

To measure these preferences, the researchers implemented a three-arm survey experiment involving a large sample of 3,820 public sector personnel. Respondents were randomly assigned to read vignette scenarios describing one of three reform pathways: the adoption of AI-assisted budgeting, an expansion of the government workforce, or the introduction of participatory budgeting, in which citizens help decide spending priorities. Random assignment ensures that any differences in reported support across the three groups can be attributed to the reform scenario itself rather than to pre-existing differences among respondents, the standard logic of experimental design in the social sciences.

The headline finding is striking. In analyses restricted to respondents who correctly recalled their assigned treatment and weighted to account for differential treatment recall, AI-assisted budgeting reforms attracted significantly higher bureaucratic support than workforce expansion. This suggests that, at least among the civil servants surveyed, the prospect of algorithmic assistance in allocating public funds is not met with the resistance that fears of automated job displacement might predict. Instead, bureaucrats appear to view AI as a more attractive reform than hiring additional personnel, perhaps because algorithmic tools promise to augment their capacity without the organizational disruptions, coordination costs, and budgetary competition that come with expanding the payroll.

When it came to participatory budgeting, however, the picture was more nuanced. The robustness analyses found no statistically significant differences in bureaucratic support between the AI-assisted budgeting treatments and the participatory budgeting treatments. In other words, civil servants were roughly equally comfortable with delegating budgetary insight to algorithms and with opening budgetary decisions to citizen participation. This equivalence is notable because the two reforms embody very different theories of legitimacy: one rests on technical optimization and data-driven objectivity, while the other rests on democratic inclusion and deliberation. The finding hints that bureaucrats may judge reforms less by their philosophical underpinnings than by more practical considerations of workload, discretion, and administrative feasibility.

The authors were careful to probe the robustness of their results. Beyond the manipulation-restricted analyses, they estimated intent-to-treat effects, which include all randomized respondents regardless of whether they remembered their assigned scenario, adjusting for covariates and incorporating organizational fixed effects to account for differences across the agencies and institutions in which respondents work. Under this more conservative specification, the estimates did not show statistically significant differences across the outcome dimensions. The divergence between the two analytical strategies underscores a familiar lesson in experimental social science: results can be sensitive to how treatment recall and analytic choices are handled, and conclusions about bureaucratic preferences should therefore be drawn with appropriate caution.

The study sits within a rapidly expanding research landscape on AI in government. Previous work has documented automation bias and selective adherence to algorithmic advice among public sector decision-makers, showing that street-level bureaucrats tend to trust AI recommendations when those recommendations confirm their existing professional judgment. Other studies have mapped the barriers to AI adoption in public organizations, examined how AI is reshaping the role of bureaucrats in different organizational contexts, and explored how public values such as efficiency and equity shape civil servants’ willingness to use AI to reduce administrative burdens. Citizen-facing research has also flourished, with survey experiments revealing when and why the public accepts the use of AI in services such as policing and local government. What distinguishes the new study is its focus on budgeting, the heart of distributive governance, and its head-to-head comparison of AI against rival reform pathways rather than against the status quo.

The implications for policymakers are significant. Governments contemplating algorithmic budgeting often worry about backlash from public employees, whose cooperation is essential for data collection, model validation, and the day-to-day operation of any decision-support system. The evidence suggests that such fears may be overblown, at least in comparative perspective: bureaucrats do not appear to view AI-assisted spending as uniquely threatening. Yet the absence of a significant advantage over participatory budgeting also suggests that algorithmic reform is not a slam dunk. Reformers cannot assume that AI carries inherent legitimacy among the administrative workforce; it competes on roughly equal footing with democratic alternatives. The practical lesson may be that the success of AI in fiscal governance will depend less on winning bureaucratic hearts and minds than on careful system design, transparent safeguards, and clear communication about how algorithmic recommendations relate to human discretion.

As governments worldwide continue to draft national AI strategies and embed machine learning in everything from tax policy optimization to healthcare allocation, understanding the preferences of the people who run the administrative state becomes ever more important. This study provides some of the first experimental evidence that, when given a choice between algorithmic budgeting and simply hiring more staff, bureaucrats lean toward the machines. Whether that preference translates into successful implementation, and whether it holds across countries, sectors, and levels of government, remains an open question that future research will need to answer.

Subject of Research: Bureaucratic support for AI-assisted public budgeting compared with workforce expansion and participatory budgeting

Article Title: Do bureaucrats prefer algorithmic budgeting? Experimental evidence on bureaucratic support for AI-assisted public spending

Article References: Cho, W., & Sagarik, D. (2026). Do bureaucrats prefer algorithmic budgeting? Experimental evidence on bureaucratic support for AI-assisted public spending. Global Public Policy and Governance, 6(2), 157-175. https://doi.org/10.1007/s43508-026-00145-z

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00145-z

Keywords: artificial intelligence, public budgeting, bureaucrats, participatory budgeting, public administration, survey experiment, algorithmic governance, fiscal policy, government reform, public finance, civil service, prefer