The scale of the discipline’s transparency deficit is striking. Surveys of top criminology journals have found that fewer than five percent of articles include preregistration, and large-scale reproduction tests place criminology near the bottom of the social and behavioral sciences, with exact reproduction achieved in just five percent of sampled articles from flagship outlets. No criminology journal currently requires data and code sharing, and the field’s open data efforts have concentrated on rare events such as school shootings and terrorism incidents rather than the everyday studies that make up the bulk of published research. Researchers who avoid archiving data often report that they believe the practice is either impossible or professionally unrewarded, a perception reinforced by the absence of institutional requirements.

What makes criminology different from fields where open data has flourished is the nature of the raw material. Victimization surveys, correctional records, and law enforcement data carry strict privacy protections that preclude public release. Researchers navigating privacy laws, institutional review requirements, and data use agreements face real professional and ethical risks in sharing records that describe criminal victimization, offending, or police conduct. Protecting the people represented in these datasets is not optional. But as Adams and Vaughn argue, privacy concerns need not foreclose transparency entirely, provided that researchers can verify which statistical relationships survive the process of manufacturing artificial records.

Synthetic data are not a new idea; statisticians have proposed them as a disclosure limitation tool since the early 1990s. The concept is straightforward: rather than publishing real records, researchers fit statistical models to the original data and draw new, artificial records from those models. The synthetic dataset preserves the useful structure of the original, including correlations and group differences, while containing no actual participants. Whether such data are fit for purpose, however, depends entirely on how they are generated and what analyses they are meant to support. General fidelity to the original distributions does not guarantee that any particular analysis will yield the same conclusions.

To test this question rigorously, Adams and Vaughn turned to a preregistered factorial vignette experiment originally published in Police Quarterly, which examined how police and human resources executives evaluate workplace profanity. The original design was methodologically challenging for synthesis: 1,351 respondents each rated four of nine vignette conditions, producing 5,180 nested observations, with outcomes measured on five-point scales covering appropriateness, professionalism, public trust, and recommended sanctions. The experimental design manipulated two factors: the target of profanity, whether directed at oneself, a colleague, or the public, and the context in which it occurred, whether derogatory, celebratory, or neutral.

The researchers used sequential classification and regression tree synthesis, implemented in the widely used synthpop package for the R statistical language. Because the experiment involved repeated responses from the same people, they first restructured the data so each respondent’s observations occupied a single row, ordered by vignette condition, preserving the experimental assignment and completion patterns. The five outcomes in each slot were then synthesized conditional on the design variables and previously synthesized outcomes. The result was a partially synthetic dataset: experimental assignments remained unchanged while all outcome values were replaced with model-generated draws, with identifiers excluded throughout to maintain deidentification.

The validation strategy was unusually thorough, examining disclosure risk, general fidelity, and utility for the specific models the data were meant to support. Disclosure diagnostics counted distinct variable combinations, duplicated rows, and exact matches across datasets, including replicated uniques, combinations appearing exactly once in each dataset that nevertheless match. General fidelity was assessed with a propensity score mean squared error, a comparison of all ten Pearson correlations among the outcomes, and Mahalanobis distance analysis using the original data’s covariance structure. Specific utility was judged by re-estimating the five mixed-effects models from the original study on the synthetic data and comparing all 20 treatment coefficients, their standard errors, and their significance classifications.

The results were remarkably strong on the measures that matter most for reproduction. Ninety percent of the 20 treatment coefficients differed from the original estimates by less than two original standard errors, with a mean absolute standardized coefficient difference of 1.03. Standard-error ratios ranged from 0.989 to 1.085, meaning model-based precision was nearly identical to the original analysis. All 20 coefficient directions and all 95 percent confidence interval classifications agreed across the two datasets, and both supported the original substantive finding: profanity directed at the public drew harsher evaluations than self-directed profanity, with smaller contrasts for colleague-directed speech. The mean absolute difference across the ten outcome correlations was just 0.015, and the propensity score measure of 0.0020 indicated the two datasets were hard to distinguish statistically.

Yet the study is candid about the limits of the approach. The two coefficients outside the descriptive benchmark both concerned personal discipline judgments, departing in opposite directions. Residual intraclass correlations, which capture how strongly a respondent’s answers are clustered together, declined modestly in the synthetic data for some outcomes, dropping from 0.515 to 0.419 for personal discipline. Extreme multivariate observations diverged in the tails of the Mahalanobis distance distribution. And critically, synthesis is not automatic privacy protection: exact matches and distinctive respondent response patterns remained despite the replacement of all outcome values, meaning release decisions still require context-specific assessment of what external information could connect patterns to people.

The implications reach well beyond one dataset. Adams and Vaughn note that limited data access may inadvertently encourage questionable research practices: surveys of criminologists have found that 53 percent report selectively underreporting results and 43 percent admit omitting nonsignificant findings. Synthetic data releases paired with executable code can expose programming errors and make analytical choices inspectable, though they cannot authenticate original observations or prove selective reporting did not occur. The authors recommend that researchers document intended uses, software versions, random seeds, and disclosure assessments; that repositories clearly distinguish synthetic records from originals; and that journal editors request executable code alongside a justified data-access plan. With recent signals that the Journal of Quantitative Criminology will soon require reproducible code and data at submission, synthetic data may arrive just in time, giving a privacy-bound discipline the tools to open its doors without betraying the people behind its records.

Subject of Research: Synthetic data generation for reproducible and privacy-preserving open science in criminology

Article Title: Synthetic Data as a Pathway to Reproducible Criminology

Article References: Adams, I. T., & Vaughn, P. E. (2026). Synthetic Data as a Pathway to Reproducible Criminology. American Journal of Criminal Justice. https://doi.org/10.1007/s12103-026-09953-9

Image Credits: AI Generated

DOI: 10.1007/s12103-026-09953-9

Keywords: synthetic data, criminology, reproducibility, open science, data privacy, synthpop, CART synthesis, survey experiment, disclosure risk, mixed-effects models, data sharing, research methods

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

The research, conducted by Gábor Hajdu of the ELTE Centre for Social Sciences, addresses a persistent gap in a literature dominated by correlational data. Decades of observational studies have linked both actual and perceived economic inequality to lower subjective well-being, but such studies cannot rule out reverse causation or confounding: people with lower incomes are both less satisfied and more likely to perceive inequality, and macroeconomic conditions such as unemployment or inflation can jointly shape perceptions and mood. Common method variance poses an additional problem, since both life satisfaction and perceived inequality are subjective self-reports that may be influenced by shared unobserved factors such as mood or personality. An experiment, by randomly assigning information, cuts through these threats to causal identification.

The design was straightforward but rigorous. In October and November 2024, respondents drawn from a Hungarian online research panel were randomly assigned to a control group or to one of two treatment groups. One treatment group read a real newspaper excerpt presenting factual evidence that economic inequality in Hungary was decreasing and below the European Union average, citing declines in the Gini coefficient and the S80/S20 income ratio as well as a sharp fall in the share of people at risk of poverty or social exclusion. The other group read an excerpt emphasizing that wealth inequality in Hungary is the second highest in Europe, with the richest one percent owning 33.5 percent of total wealth and the gap between the elite and the rest of society growing. Both texts were written in standard journalistic style, drawn from well-known Hungarian news sources, and contained no explicit emotional or normative wording.

The results were striking. Respondents in the negative information treatment reported significantly lower life satisfaction than the control group, with a regression coefficient of −0.309 on an 11-point satisfaction scale, statistically significant at conventional levels. The positive information treatment also produced a negative point estimate, but it was smaller and not statistically significant. Notably, simply reading and thinking about inequality — regardless of direction — appeared to strengthen agreement with the statement that income differences in Hungary are too large, indicating that normative judgments respond to attention itself, not merely to the substance of the news. Robustness checks that excluded very fast or inattentive respondents, and samples restricted to those who reported recalling the texts, produced if anything slightly larger effects.

The manipulation check confirmed that the treatments shifted perceptions as intended. Across three separate measures of descriptive inequality perceptions — a subjective Gini coefficient constructed from respondents’ earnings estimates for five occupations, a Gini derived from a “shape of society” diagram task, and a twofold measure combining both — the two treatment groups differed significantly, with differences ranging from 12 to 24 percent of a standard deviation. Yet the effect on life satisfaction did not track these descriptive shifts. Instead, the pattern points toward normative evaluations and socio-psychological responses as the operative channels, consistent with earlier findings that unfairness judgments matter more for well-being than perceived inequality levels per se.

Perhaps the most consequential finding concerns who was affected. Splitting the sample by socioeconomic status revealed pronounced heterogeneity: among respondents with above-median household income or higher education, treatment effects on life satisfaction were essentially zero, while among those with lower incomes or primary and vocational education the negative effects were larger than in the full sample. The burden of inequality-related information thus falls disproportionately on the very people who are already most vulnerable. This mirrors prior evidence on inequality aversion and on the moderating role of socioeconomic status in the perceived inequality–well-being relationship, and it carries policy implications: public communication about inequality may function as a regressive psychological tax.

Mechanism analysis identified subjective social status as the primary pathway. Respondents rated their position on a ten-rung social ladder, and formal causal mediation analysis showed that this single construct accounted for approximately half of the total treatment effect, with an average causal mediation effect of −0.158. The normative evaluation of inequality played only a marginal mediating role. The substantial remaining direct effect, though imprecisely estimated, suggests that additional cognitive and affective mechanisms — intensified social comparison, status anxiety, heightened concerns about fairness, or worries about societal cohesion — are also likely at work but were not fully captured by the measured mediators. This aligns with theoretical frameworks emphasizing relative deprivation, in which perceived inequality raises aspirations and widens the perceived gap between one’s own position and relevant comparison groups.

The Hungarian setting matters for interpretation. Post-socialist societies tend to exhibit stronger inequality aversion than Western Europe, a legacy documented in studies of preferences formed under communism, and social mobility in Hungary is substantially lower than in Germany — the context of the only comparable prior experiment. Since high mobility attenuates the negative well-being effects of perceived inequality while low mobility amplifies them, a stronger effect in Hungary is plausible. In the earlier German study by John and colleagues, inequality information shifted emotional responses but not life satisfaction; the Hungarian results, which benefited from a true control group, show that exposure to inequality news reduces life satisfaction in absolute terms, not merely relative to alternative framings. Hungary’s politically salient public discourse on elite wealth concentration makes the finding especially relevant.

The study’s limitations are acknowledged candidly. The two treatments were not perfectly symmetrical — the positive text emphasized income and poverty indicators while the negative text focused on wealth concentration — a trade-off accepted to preserve ecological validity through real journalistic texts, though the primary causal comparisons were anchored to the no-information control group. The perception measures, based on numerical estimates and a single normative item, may not capture the full multidimensionality of how people conceive of inequality. And the single-country design, while extending the geographic scope of the experimental literature beyond Western Europe, leaves open questions about generalizability across cultural and institutional contexts. Data and code are publicly available, and the author notes the research was funded by the Hungarian National Research, Development and Innovation Office without funder involvement in the analysis.

For the broader debate on inequality and happiness, the implications are substantial. If objective inequality affects well-being only insofar as it shapes perceptions — as some scholars argue — then the communication environment surrounding inequality becomes a policy variable in its own right. Media attention to inequality has been shown to depress life satisfaction independently of inequality levels, and people appear not to adapt to it the way they adapt to long-run inequality. The present findings demonstrate experimentally that information alone, without any change in material conditions, moves life satisfaction through perception-based mechanisms of status evaluation and fairness judgment. As economic disparities remain high across much of the world and inequality dominates headlines, understanding these psychological channels is no longer optional for researchers or policymakers: how societies talk about inequality may matter nearly as much as the inequality itself.

Subject of Research: The causal effect of perceived economic inequality on life satisfaction, tested through a randomized online survey experiment in Hungary.

Article Title: Perceived Economic Inequality and Life Satisfaction: Evidence from a Survey Experiment

Article References: Hajdu, G. (2026). Perceived Economic Inequality and Life Satisfaction: Evidence from a Survey Experiment. Social Indicators Research, 184(2), Article 35. https://doi.org/10.1007/s11205-026-03928-3

Image Credits: AI Generated

DOI: 10.1007/s11205-026-03928-3

Keywords: economic inequality, life satisfaction, subjective well-being, survey experiment, inequality perceptions, subjective social status, Hungary, relative deprivation, status anxiety, social indicators research, happiness economics, media effects

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