Legal Challenges of Using AI and Big Data in Public Administration: Administrative Liability, Data Protection, and Public Services Efficiency
Bibliographic record
Abstract
This study investigates the evolving legal challenges posed by the integration of artificial intelligence and big data in public administration.Through a mixed-method approach, combining doctrinal legal analysis, case study review, and empirical dataset evaluation, it examines how current laws respond to emerging risks in administrative liability, data protection, and service automation.A novel Legal Risk Index (LRI) was developed to quantify regulatory sensitivity across jurisdictions and application domains, revealing that systems in welfare fraud detection and biometric surveillance face the highest legal scrutiny, with LRI scores reaching critical thresholds in over 70% of examined cases.The study analyzed a dataset of 140+ public sector AI deployments across Europe, offering a concrete empirical base.The paper compares AI governance strategies in the EU, UK, US, and MENA, highlighting disparities in oversight, enforcement, and public accountability.The findings show that legal maturity, not just technological advancement, is key to responsible deployment.Major contributions include the introduction of a cross-jurisdictional legal risk framework, evidencebased policy recommendations, and a structured model for liability allocation in AI-driven decisions.This research offers practical insights for regulators and public institutions seeking to balance innovation with rights-based 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 | 0.086 | 0.225 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".