Generative Artificial Intelligence Systems in the Fight Against Corruption: Potential, Threats and Prospects for Ukraine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Corruption remains one of Ukraine's most pressing challenges, undermining the rule of law, hindering economic development, and eroding public trust in state institutions. In the contemporary digital transformation era, generative Artificial Intelligence (AI) systems present new opportunities for combating corruption through automated solutions for financial flow analysis, anomaly detection, and corruption risk assessment. However, deploying such technological systems raises significant legal, ethical, and technical concerns. This article analyses the potential and challenges of applying generative AI systems in Ukraine's anti-corruption policy. Through comparative analysis of international experience, the study identifies effective methods for implementing AI in Ukraine's law enforcement and governance practices, considering the country's legislative framework and political context. The research examines risks associated with AI implementation, including algorithmic manipulation, cybersecurity threats, data protection concerns, and ethical challenges. The authors propose recommendations for adapting AI technologies to Ukraine's anti-corruption efforts, including developing regulatory frameworks, introducing algorithmic accountability, implementing ethical AI standards, and strengthening international cooperation. The findings demonstrate that, with proper regulation and oversight, generative AI can enhance government transparency and reinforce the rule of law in anti-corruption efforts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it