| --- |
| license: apache-2.0 |
| library_name: scikit-learn |
| pipeline_tag: tabular-classification |
| tags: |
| - ai-governance |
| - responsible-ai |
| - risk-classification |
| - scikit-learn |
| - synthetic-data |
| datasets: |
| - aigovdev/ai-governance-scenarios |
| --- |
| |
| # Governance Risk Classifier |
|
|
| A small experimental baseline for classifying synthetic AI governance scenarios into coarse engineering risk tiers. |
|
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| The model is part of the AIGovDev public AI governance engineering portfolio. |
|
|
| ## Model architecture |
|
|
| The pipeline uses: |
|
|
| - categorical feature preprocessing |
| - one-hot encoding |
| - logistic regression |
| - balanced class weights |
|
|
| Input features: |
|
|
| - sector |
| - impact |
| - decision autonomy |
| - human oversight |
| - post-deployment monitoring |
| - traceability |
| - technical documentation |
|
|
| ## Target labels |
|
|
| The original governance labels are mapped into three coarse tiers: |
|
|
| - `low` and `limited` → `lower` |
| - `high` → `high` |
| - `unacceptable` → `unacceptable` |
|
|
| These labels are engineering annotations only. |
|
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| They are **not legal classifications** and must not be interpreted as determining compliance with the EU AI Act or any other regulation. |
|
|
| ## Training data |
|
|
| The model is trained on: |
|
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| `aigovdev/ai-governance-scenarios` |
|
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| The current dataset contains 12 synthetic scenarios: |
|
|
| - 6 `lower` |
| - 3 `high` |
| - 3 `unacceptable` |
|
|
| The scenarios were manually constructed for experimentation and demonstration. |
|
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| They are not production data. |
|
|
| ## Evaluation |
|
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| The current prototype uses stratified 3-fold cross-validation. |
|
|
| Results: |
|
|
| | Metric | Score | |
| | --- | ---: | |
| | Accuracy | 1.000 | |
| | Macro precision | 1.000 | |
| | Macro recall | 1.000 | |
| | Macro F1 | 1.000 | |
|
|
| Confusion matrix: |
|
|
| lower 6 0 0 |
| high 0 3 0 |
| unacceptable 0 0 3 |
| |
| These results must be interpreted with extreme caution. |
|
|
| The dataset contains only 12 synthetic, manually constructed examples. The perfect cross-validation result is therefore primarily evidence that the current feature patterns are easily separable within this toy dataset. |
|
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| It is **not evidence of real-world generalization**, production readiness, regulatory validity, or calibrated governance-risk estimation. |
|
|
| ## Intended use |
|
|
| This model is suitable for: |
|
|
| - educational experiments |
| - AI governance pipeline prototyping |
| - demonstrating transparent tabular ML workflows |
| - testing governance-risk feature engineering |
| - portfolio and research-oriented demonstrations |
|
|
| ## Not intended for |
|
|
| The model must not be used as: |
|
|
| - a legal compliance classifier |
| - an EU AI Act classification system |
| - a production governance decision engine |
| - a substitute for legal or technical risk assessment |
| - a validated risk-scoring system |
|
|
| ## Reproducibility |
|
|
| The repository contains: |
|
|
| - `src/train.py` |
| - `requirements.txt` |
| - `model.joblib` |
| - `metrics.json` |
|
|
| The training script downloads the public dataset directly from Hugging Face and reproduces the model pipeline and evaluation. |
|
|
| ## Limitations |
|
|
| Major limitations include: |
|
|
| - extremely small dataset |
| - synthetic scenarios |
| - manually constructed labels |
| - simplified governance dimensions |
| - no external validation set |
| - no calibration analysis |
| - no real deployment data |
|
|
| ## Future work |
|
|
| Possible extensions include: |
|
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| - larger governance scenario datasets |
| - independently annotated examples |
| - real-world control evidence |
| - lifecycle and incident features |
| - probability calibration |
| - external validation |
| - benchmark comparison |
| - legal-risk separation from engineering-risk signals |
|
|
| ## AIGov |
|
|
| AIGov builds infrastructure for transparent, auditable, and accountable AI systems. |
|
|
| Website: https://govbase.dev |
|
|
| ## License |
|
|
| Apache-2.0 |
|
|
| ## Inference |
|
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| The repository includes `inference.py` for local predictions. |
|
|
| Example: |
|
|
| ```bash |
| python inference.py --json '{ |
| "sector": "financial_services", |
| "impact": "high", |
| "decision_autonomy": "automated", |
| "human_oversight": "none", |
| "monitoring": "none", |
| "traceability": "none", |
| "technical_documentation": "partial" |
| }' |
| ``` |
|
|
| Example output: |
|
|
| ```json |
| { |
| "risk_tier": "unacceptable", |
| "class_probabilities": { |
| "high": 0.24612, |
| "lower": 0.08766, |
| "unacceptable": 0.66622 |
| } |
| } |
| ``` |
|
|
| The returned class probabilities are model scores from the logistic-regression baseline. They should not be interpreted as calibrated probabilities of legal or real-world governance risk. |
|
|
