Post-GDPR AI: Federated Audit Trails and Compliance Automation for Data Engineering
Bibliographic record
Abstract
Implementation of the General Data Protection Regulation (GDPR) has required a paradigm change in the gathering, processing and management of data across decentralized systems. Conventional centralized co-productions of compliance processes fail to hold up in providing accountability and transparency to the AI-driven data engineering workflow practices. The current paper envisions a new framework that combines federated audit trails, compliance automation based on AI to withstand the regulatory challenges presented by the GDPR after it became law. By applying the principles of federated learning, the proposed architecture also decentralizes audit log generation and verification without reducing data privacy creating provenance of data and consent tracking of users in the context of multicloud settings. In addition, smart policy engines powered by machine learning will automate compliance checks in real-time and identify violations as they occur on an ongoing basis and adapt to new legal standards. Experimental tests on both synthetic and real-world data have shown large gains in the regulatory traceability, scalability, and audit efficiency of a system relative to conventional logging mechanisms. This paper sets the stage towards privacy-respecting, explainable, and legally-accountable artificial intelligence infrastructures in the age of data protection around the world.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".