Model management for regulatory compliance
Bibliographic record
Abstract
Software has come to mediate many of the activities in life, including financial service platforms, social networks and vehicle control. As a result, governing bodies have responded to this trend by creating standards and regulations to address issues such as safety and privacy. In this context, the compliance of software development to standards and regulations has emerged as a key issue. For software development organizations, compliance is a complex and costly goal to achieve. They may have to comply with multiple standards due to multiple jurisdictions or to address different aspects of the software and these may overlap and conflict with each other. The evolution of standards must be tracked and changes assessed. Evidence for claims of compliance must be collected and managed. Finally, maintaining families of related software products (product lines) further multiplies the effort. In this paper, we propose to exploit the connection between the field of model management and the problem of compliance management and explore how to use model management techniques to address software compliance management issues.
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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 | 0.029 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".