The study, published in Frontiers in Political Science, was conducted by a team from Sam Ratulangi University in Manado and collaborators in Jakarta and Yogyakarta. Led by Joyce Jacinta Rares of the Department of Public Administration, the researchers set out to answer a deceptively simple question: how does the interaction between national policy content and local implementation context shape what actually happens on the ground? Their answer, drawn from months of fieldwork across one of Indonesia’s most geographically fragmented provinces, is a cautionary tale for any government betting on uniform policy designs to deliver distributive justice.

North Sulawesi was a deliberate choice of case study. The province stretches across mainland districts and remote island chains, including the Sangihe and Talaud archipelagos that lie closer to the Philippines than to the provincial capital. Infrastructure quality, transport access, and administrative capacity vary dramatically from one regency to the next. This heterogeneity makes the province an ideal laboratory for observing what happens when a nationally standardized program collides with local realities that no central guideline anticipated.

Between June and August 2025, the researchers conducted in-depth interviews with eight key informants, including representatives of two central ministries, managers of the Nutrition Fulfillment Service Units known as SPPG, the local kitchens that prepare and distribute meals, and school principals from Manado City, Minahasa Regency, the Sangihe Islands, and the Talaud Islands. They supplemented these interviews with direct field observations at kitchen facilities, including the SPPG at Soataloara II in Sangihe and the SPPG at Melonguane in the Talaud Islands, and with an analysis of policy documents and official reports. The team applied qualitative thematic analysis, triangulating evidence across sources and drawing on Grindle’s policy implementation framework alongside Michael Lipsky’s theory of street-level bureaucracy, which holds that frontline officials exercise substantial discretion as they reconcile standardized rules with messy local conditions.

The first major finding concerns targeting. Although MBG is nominally universal, the researchers found that the selection of beneficiary schools was driven overwhelmingly by administrative readiness rather than by social vulnerability. Schools with strong documentation, adequate facilities, and robust bureaucratic reporting were far more likely to be enrolled in the program than schools in remote, low-income areas, even though the latter often faced the greatest nutritional need. The authors describe this as a shift from substantive rationality, which would prioritize students’ actual nutritional deficits, to procedural rationality, which rewards administrative order and auditability. In the language of public management, this is a classic case of goal displacement: the means of the bureaucracy, its procedures and compliance mechanisms, gradually replace the original ends of the policy.

The consequences are starkly visible in the island regencies. A principal at Ebenhaezar Christian Elementary School in Melonguane, Talaud Islands, told the researchers that food deliveries do not always arrive on schedule because transportation to the island depends heavily on sea conditions, and that when weather deteriorates, meal distribution is delayed and maintaining food quality and freshness becomes increasingly difficult. Another principal, at Apengsembeka Inpres State Elementary School in the Sangihe Islands, noted that implementation procedures are identical to those applied in mainland areas even though logistical realities differ profoundly, with higher transport costs and longer delivery times that current arrangements fail to reflect. Urban schools in Manado, by contrast, tended to receive services more consistently.

The second finding concerns power. MBG is formally designed as a cross-sectoral program, but the researchers documented deeply asymmetrical relations among implementing actors. Strategic decisions about program design, operational standards, and implementation mechanisms are concentrated at the national level, while local governments and schools function largely as technical executors. A manager of the SPPG in PAAL Dua, Manado, explained that their responsibility is to implement the technical guidelines issued by the central government, with very limited authority to modify operational procedures because major decisions, including menu standards and implementation mechanisms, must comply with national guidelines. The result is a structural squeeze: frontline managers bear direct responsibility for kitchen operations, menu planning, and distribution, yet lack the authority to adapt these operations to conditions the national standards never envisioned.

Caught between rigid rules and unforgiving geography, implementers resort to informal adaptations. The SPPG manager in Manado described occasions when meal portions had to be reduced because of budget limitations and the rising cost of transporting supplies, adjustments made solely to keep the program running. Elsewhere, managers substituted unavailable ingredients with locally obtainable alternatives, despite rules prohibiting menu changes without prior approval. The researchers characterize these practices not as acts of resistance but as survival strategies, what they call forced deviations produced by the policy design itself. Because these adaptations are individual and non-institutionalized, they generate inconsistencies in service quality across regions while leaving the underlying design flaws unaddressed.

The deeper problem, the authors argue, is a spatial disconnection at the heart of the policy. MBG’s operational guidelines assume relatively uniform infrastructure, continuous market access, and manageable price fluctuations, assumptions that may hold in densely connected mainland regions but collapse in archipelagic terrain. The program relies on standardized per-portion cost frameworks set at national or overly broad regional levels, which fail to capture the vastly higher logistics costs of delivering a single meal to the outer islands of Sangihe or Talaud compared with Manado. When these cost differentials are not accommodated in budget design, food quality in remote areas is effectively sacrificed to subsidize transportation expenses. Rigid menu standardization compounds the problem, ignoring local dietary preferences and local food availability, and forgoing the potential multiplier effects that procurement from local farmers, fishers, and small businesses could provide.

Perhaps the most provocative conclusion is what the researchers call the paradox of universalism. Universal policies are theoretically meant to eliminate stigma and guarantee basic rights without discrimination, yet when implemented across structurally unequal landscapes without affirmative mechanisms, they can reproduce the very inequalities they aim to erase. Schools serving predominantly low-income students in remote areas experience delays, degraded quality, or outright exclusion due to administrative requirements, while better-resourced urban schools receive consistent, higher-quality service. This regressive pattern, in which benefits flow disproportionately to those already better off or easier to reach, means that MBG’s universal label increasingly functions as a symbol of state presence and political commitment rather than as an effective instrument of redistribution.

The study’s implications reach well beyond Indonesia. The authors recommend geographically differentiated budgeting that provides higher per-meal allocations for remote island regions, greater operational flexibility for local governments and implementing units in determining menus, procurement, and distribution schedules, and targeting mechanisms grounded in empirical indicators of social vulnerability rather than administrative readiness. They frame these reforms through the principle of asymmetric decentralization, which holds that regions with distinct geographical and socio-economic characteristics require differentiated policy arrangements rather than uniform treatment. Without such flexibility, they warn, even the most ambitious welfare policies risk becoming promises on paper for peripheral communities. The future success of programs like MBG, they conclude, will not be determined by uniform menus delivered from one end of the archipelago to the other, but by the system’s sensitivity and capacity to adapt to local rhythms across Indonesia’s vast and varied landscape.

Subject of Research: Implementation of Indonesia's universal free school meal program and its social exclusion effects in North Sulawesi

Article Title: Implementation of universal welfare policies in the Global South: a case study of Indonesia’s free nutritious meal program

Article References: Rares, J. J., Mamentu, M., Ridha, M., & Abidin, Z. (2026). Implementation of universal welfare policies in the Global South: a case study of Indonesia’s free nutritious meal program. Frontiers in Political Science, 8, Article 1845044. https://doi.org/10.3389/fpos.2026.1845044

Image Credits: AI Generated

DOI: 10.3389/fpos.2026.1845044

Keywords: Indonesia, Free Nutritious Meal Program, welfare policy, policy implementation, Global South, North Sulawesi, social exclusion, street-level bureaucracy, school feeding, stunting, decentralization, archipelagic logistics

Street-level bureaucrats—the police officers, inspectors, social workers, teachers, and regulators who interact directly with citizens—have long occupied a paradoxical position in public administration scholarship. Since Michael Lipsky’s foundational work in 1980, researchers have recognized that these workers exercise substantial discretion: they cannot possibly apply every rule to every case with mechanical precision, so they inevitably make choices about how vigorously to enforce, whom to prioritize, and when to look the other way. What has remained harder to pin down is the psychology behind those choices. The new study, conducted by Yingwei Wang of East China Normal University and Hong Pan of Chongqing University, turns to expectation theory to supply that missing psychological mechanism.

Expectation theory holds that people’s motivation and behavior are shaped not only by outcomes they have experienced but by outcomes they anticipate. In economics, this logic underpins studies of inflation expectations and consumer spending; in education, it animates the expectancy-value theory of motivation developed by Allan Wigfield and Jacquelynne Eccles. Wang and Pan extended this framework to the enforcement context, proposing that a bureaucrat’s expectations about a policy’s time horizon and about the level of resistance it will provoke should influence how strictly that bureaucrat is inclined to enforce it—and that self-efficacy, the belief in one’s own capacity to execute the task successfully, should mediate that influence.

To test these propositions, the researchers designed a survey experiment with a two-by-two factorial structure. Participants were randomly assigned to scenarios that varied along two dimensions: time expectations, manipulated as either long-term or short-term policy duration, and resistance expectations, manipulated as either strong or weak anticipated pushback from those affected by the policy. This experimental design is a hallmark of behavioral public administration, a movement that has gained momentum since a landmark 2017 review by Stephan Grimmelikhuijsen and colleagues argued for combining the analytical tools of psychology with the substantive questions of public administration. By randomly assigning expectations rather than merely measuring them, the researchers could draw causal inferences about their effects—something observational studies of bureaucratic behavior rarely permit.

The results were striking in their symmetry. Short-term expectations weakened street-level bureaucrats’ strict enforcement behavior tendencies more than long-term expectations did. In other words, when officials believed a policy was a passing campaign rather than a durable commitment, their inclination to enforce it rigorously declined. This finding resonates with a broader literature on policy feedback and bureaucratic compliance: work by Peter Kettunen and colleagues, and by researchers examining how stability in policy signals builds trust, has suggested that long-term expectations foster a sense of seriousness and legitimacy that short-term initiatives lack. A policy framed as permanent invites officials to invest in it; a policy framed as temporary invites them to wait it out.

Resistance expectations worked in the opposite direction. Strong resistance expectations strengthened strict enforcement tendencies more than weak resistance expectations did. This result may appear counterintuitive at first glance—one might expect officials facing stiff opposition to back off. But the authors’ framework, and the surrounding literature on policy resistance, suggests a different interpretation: anticipated resistance can signal that a policy matters, that enforcement is consequential, and that a bureaucrat’s role in defending it is legitimate and necessary. Related scholarship by Gómez and Padró on anticipated resistance and strategic adaptation, and by Peters, Pierre, and Stoker on policy enforcement under pressure, has similarly portrayed resistant contexts as ones in which strict adherence becomes a deliberate, even defiant, choice rather than a default.

Perhaps the most theoretically significant finding concerns the mechanism linking expectations to behavior. Self-efficacy mediated the relationship between policy expectations and enforcement tendencies. That is, expectations did not simply push behavior directly; they worked by changing how capable bureaucrats felt. Long-term expectations and strong resistance expectations appear to bolster the sense that enforcement is achievable and meaningful, which in turn strengthens the tendency toward strict enforcement. This mediating role aligns with a growing body of work on self-efficacy in public service, including studies by Bevan and Hood on perceived policy success and bureaucratic performance, by López and Stoker on expectations of success and implementers’ actions, and by Walker on self-efficacy and public sector innovation. Together, these findings suggest that confidence is not a fixed trait but a dynamic state that policy communication can cultivate or erode.

The Chinese setting gives the findings particular weight. China’s governance system relies heavily on campaigns—intensive, time-limited pushes to achieve specific policy goals, from environmental inspection to pandemic control—that are implemented by regulatory street-level bureaucrats. Previous experimental work by the same authors examined how information frameworks affect compliance with policies that challenge traditional customs, and how digital regulation shapes enforcement attitudes during pandemic emergencies. Other Chinese studies have explored gender, situational visibility, and discretionary decision-making among regulatory bureaucrats. The new study adds expectation management to this toolkit, implying that how campaigns are framed—whether as enduring commitments or temporary drives—may systematically alter the rigor of frontline enforcement across China’s vast administrative apparatus.

The practical implications extend well beyond China. Governments everywhere struggle with the gap between policy design and street-level implementation, a gap that scholars from Lipsky to Tummers and Bekkers have documented across decades and continents. If short-term expectations weaken strict enforcement, then governments that cycle policies rapidly, or that announce initiatives without signaling durability, may be quietly undermining their own front lines. Conversely, if anticipated resistance strengthens enforcement, then preparing officials psychologically for pushback—rather than shielding them from information about it—may produce more consistent implementation. The mediating role of self-efficacy points to a further lever: training, supervisory support, and success feedback that build implementers’ confidence could amplify the effects of well-managed expectations. Hassan and colleagues’ work on supervisory support as a moderator of discretion, and Luthans and Youssef’s research on positive organizational behavior, both suggest concrete avenues for such interventions.

The study also carries methodological significance for the field. Survey experiments of this kind—randomizing vignette conditions and measuring behavioral tendencies—remain relatively rare in studies of enforcement, where access to frontline officials is difficult and behavior is hard to observe directly. By demonstrating that two theoretically distinct dimensions of expectation can be manipulated independently and that their effects operate through a measurable psychological mediator, Wang and Pan have provided a template that researchers in other countries and policy domains can adapt. The theoretical payoff is an expanded framework of street-level bureaucratic behavior, one in which anticipation, confidence, and discretion interact rather than operate in isolation. The practical payoff is a reminder that the fate of any policy is decided not only in ministries and legislatures, but in the expectations carried by the officials who meet the public—and that those expectations, unlike many drivers of bureaucratic behavior, can be deliberately shaped.

Subject of Research: How policy expectations and self-efficacy shape street-level bureaucrats' enforcement behavior tendencies in China

Article Title: Policy expectations, self-efficacy, and street-level bureaucrats’ enforcement behavior tendencies: a survey experiment in China

Article References: Wang, Y., & Pan, H. (2025). Policy expectations, self-efficacy, and street-level bureaucrats’ enforcement behavior tendencies: a survey experiment in China. Global Public Policy and Governance, 5(3), 274-296. https://doi.org/10.1007/s43508-025-00121-z

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00121-z

Keywords: street-level bureaucracy, policy expectations, self-efficacy, enforcement behavior, survey experiment, policy implementation, behavioral public administration, China, discretion, expectation theory, policy resistance, public administration

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 research was conducted by Ge Wang, Haixin Teng, and Zengyang Xu of the School of Public Administration at Central China Normal University in Wuhan, and it addresses one of the most persistent blind spots in the study of algorithmic governance. Scholars have long argued that automation transforms street-level bureaucracy, echoing Michael Lipsky’s classic insight that frontline workers are the ultimate policymakers because they exercise discretion in every individual encounter. Earlier work by Bovens and Zouridis described a drift from street-level to system-level bureaucracy, in which decision-making migrates from human judgment to automated systems. But the new study asks a sharper question: what happens to how bureaucrats enforce — not just what they decide — when AI enters the loop, and how does felt accountability reshape that relationship?

To answer it, the team designed a preregistered survey experiment embedded in the real-world context of honking violations, a common and familiar enforcement scenario for traffic police. Participants were 356 street-level police officers drawn from a representative sample, making this one of the more methodologically robust attempts to measure enforcement style directly among practitioners rather than students or hypothetical respondents. Invalid questionnaires were excluded before hypothesis testing, based on substantial missing data, incomplete experimental responses, or failure to pass embedded attention-check items, and the experimental design was preregistered to guard against selective reporting. Data supporting the findings are available from the corresponding author upon reasonable request, and the preregistration is publicly archived on the Open Science Framework.

The experiment crossed two manipulations: whether officers received AI support in the enforcement scenario, and which type of accountability they experienced. In public administration, accountability is conventionally divided into process accountability, where officials must justify the procedures and reasoning behind their decisions, and outcome accountability, where they are judged by the results those decisions produce. Decades of psychological research, from Tetlock onward, have shown that these two forms of felt accountability trigger distinct information-processing strategies, often in opposite directions. The Chinese enforcement context makes the setting particularly instructive, given its documented history of campaign-style enforcement and shifting regulatory styles among frontline officials.

The core findings are cleanly delineated. AI-supported enforcement, on its own, pushed officers toward a more formal and coercive style of enforcement — rule-invoking, sanction-first, procedural — while weakening the educational component, the practice of explaining, persuading, and teaching violators why compliance matters. This matters because enforcement style is not cosmetic. A substantial body of regulatory scholarship, including work by May and Wood on inspectors at the regulatory front lines and by Braithwaite and colleagues on enforcement pyramids, links style directly to compliance outcomes, citizen trust, and the legitimacy of the state. A coercive turn among officers algorithmically nudged into formalism could therefore ripple outward into how millions of daily encounters between citizens and the state actually feel.

Accountability, meanwhile, produced effects of its own that align closely with prior experimental literature. Officers who felt process accountability gravitated toward a more educational, prioritization-focused, and accommodative enforcement style — they reasoned more about how and why they enforced, reserved harsh measures for the cases that mattered most, and left more room for discretion and dialogue. Officers under outcome accountability moved in the opposite direction, reinforcing a more formal and coercive posture, consistent with the idea that being judged purely on results encourages defensive, box-ticking enforcement designed to be blame-proof.

The most consequential and arguably most surprising result is the interaction. Process accountability did not buffer officers against the algorithmic push toward formalism and coercion; it intensified it. Under process accountability, the presence of AI support further reinforced formal and coercive enforcement while further weakening the educational approach. The authors’ interpretation, grounded in the combined-effects framework they develop, is that when officers must justify their processes, the documented, auditable recommendation of an algorithm becomes an attractive anchor — a defensible procedural basis for action. Following the machine feels procedurally safe, and in doing so officers may shed the relational, educative practices that algorithmic systems cannot capture or document. The accountability mechanism designed to make officers more thoughtful may, in the presence of AI, make them more mechanical.

These findings carry immediate implications for governments racing to deploy AI in policing, from automated violation detection to algorithmic risk assessment and case triage. The study suggests that technology procurement and accountability reform cannot be designed in isolation. An agency that pairs algorithmic enforcement tools with process-oriented oversight — often considered the ethically preferable arrangement — may inadvertently deepen the very dehumanization of frontline encounters that critics of algorithmic governance fear. Conversely, outcome accountability’s independent drift toward coercion compounds the problem. If policymakers want AI to augment rather than hollow out street-level discretion, the results imply that accountability frameworks must be engineered to explicitly protect and incentivize the educational, judgment-rich dimensions of enforcement that machines neither practice nor document.

The study also speaks to a growing theoretical conversation about artificial discretion — the hybrid of human and machine judgment described by Young, Bullock, and Lecy — and about why bureaucrats trust AI recommendations, with recent experimental work suggesting that confirmation of professional judgment drives algorithmic acceptance. By demonstrating that enforcement style is a joint product of technology and institutional design, the Chinese policing experiment moves the field beyond the simple question of whether bureaucrats accept algorithmic advice toward the richer question of what they become when they do. As cities worldwide wire AI into everything from traffic cameras to welfare fraud detection, the transformation of the frontline worker may prove to be the most important, and least visible, policy outcome of the algorithmic state.

Subject of Research: Experimental study of how AI support and accountability mechanisms transform street-level police enforcement styles

Article Title: Artificial intelligence, accountability mechanisms, and the transformation of street-level enforcement style: experimental evidence from frontline policing

Article References: Wang, G., Teng, H., & Xu, Z. (2026). Artificial intelligence, accountability mechanisms, and the transformation of street-level enforcement style: experimental evidence from frontline policing. Global Public Policy and Governance, 6(2), 176-198. https://doi.org/10.1007/s43508-026-00146-y

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00146-y

Keywords: artificial intelligence, street-level bureaucracy, policing, enforcement style, accountability, process accountability, outcome accountability, algorithmic governance, police discretion, survey experiment, public administration, coercion