Towards Agile and Transparent Data Management: New Generative AI-Driven Tools for Data Governance
Bibliographic record
Abstract
Companies and public organizations around the world are facing unprecedented challenges due to the ongoing digital transformation. These changes have raised numerous concerns related to data privacy, the ethical use of personal information, and organizational transparency. In Quebec, a region of Canada, new legislation now requires organizations to minimize the amount of data they collect, ensure strict access controls, and provide individuals with the ability to access, correct, or request the removal of their personal information held by an organization. Hydro-Québec, Quebec’s largest public energy provider, aims to be a pioneer in ethical and efficient data management. In this study, we explore new generative artificial intelligence (AI) tools to enhance data transparency for Hydro-Quebec customers. We present a customized generative AI approach, leveraging GPT-4 via Azure OpenAI Studio, to automatically generate compliant French descriptions for public data requests. The tool is implemented as a new Python library, capable of producing high-quality descriptions efficiently. It achieved a 90.48% human evaluation score, outperforming Collibra AI (73.81%) and Databricks AI (71.03%). Validation through cosine similarity, METEOR scores, and LLM-as-a-Judge assessments confirmed the solution’s superiority, highlighting its potential to drive transparent digital transformation and promote ethical data 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".