AI in anti-corruption governance: bilingual policy framework evaluation
Bibliographic record
Abstract
Purpose This study aims to examine the potential of artificial intelligence (AI) systems to generate innovative anti-corruption measures for martial law governance and post-conflict reconstruction, addressing gaps in traditional policy frameworks designed for peacetime implementation. Design/methodology/approach A mixed-methods design used six top AI systems – ChatGPT-4, ChatGPT-4o, ChatGPT-3.5, GitHub Copilot, Google GEMINI and Anthropic Claude – with bilingual English and Ukrainian queries. This study used systematic cross-validation procedures, temporal consistency verification and comprehensive content analysis over a 50-day research period from April to July 2024. A total of 216 responses were evaluated with standardized scoring matrices that assessed coherence, relevance and feasibility using five-point scales. The methodology integrated quantitative analysis of AI-generated responses with qualitative assessment of contextual appropriateness, ensuring robust evaluation of AI capabilities in crisis governance contexts. Findings AI systems demonstrated significant capability in identifying strategic gaps and proposing adaptive frameworks absent from Ukraine’s Anti-Corruption Strategy for 2021–2025. Notable variations emerged across linguistic contexts, with English-language responses showing greater analytical depth. Claude and ChatGPT-4 exhibited superior contextual understanding, while all systems identified five common anti-corruption measures: transparency initiatives, judicial reform, institutional strengthening, public engagement and education programs. However, critical limitations included contextual disconnection from existing Ukrainian institutions and reliance on pre-war training data. Originality/value This study introduces a pioneering bilingual methodology, evaluating AI-generated anti-corruption policies in both English and Ukrainian, addressing the unique challenges of martial law governance. It provides the first systematic evaluation of AI-generated policies in conflict contexts, offering practical frameworks for integrating AI with crisis governance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.083 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".