COMPLIANCE-AWARE MACHINE LEARNING PIPELINES: ANALYTICAL MODELLING OF REGULATORY CONSTRAINTS IN AUTOMATED DECISION SYSTEMS
Bibliographic record
Abstract
The increasing reliance on artificial intelligence (AI) for high-stakes decisions has heightened the importance of the regulatory compliance of its usage, ethical responsibility, and transparency in the algorithms.The presented paper proposes a structure that enables compliance-conscious machine learning pipelines, which is intended to introduce legal, ethical, and governance restrictions in the ML lifecycle.In contrast to the post-hoc auditing methods, the suggested model incorporates compliance on the algorithmic level by restraining regularization by constraints and rule validation based on logic.The framework was empirically tested with the aid of the AI Fairness Dataset (COMPAS subset) suggested in Kaggle to measure the trade-offs between the model performance, fairness, and traceability.The findings indicate that compliance research in the form of compliance-based prediction accuracy decreases by less than 3%, and fairness compliance increases by more than 20% with almost complete audit traceability.Additionally, a Continuous Compliance Learning (CL) mechanism is a part of the MLOps loop that guarantees adaptive compliance with the Srikumar Nayak https://iaeme.com/
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".