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aigovdev
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governance-risk-classifier

Tabular Classification
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scikit-learn
ai-governance
responsible-ai
risk-classification
synthetic-data
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governance-risk-classifier
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---
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.

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.

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:

`aigovdev/ai-governance-scenarios`

The current dataset contains 12 synthetic scenarios:

- 6 `lower`
- 3 `high`
- 3 `unacceptable`

The scenarios were manually constructed for experimentation and demonstration.

They are not production data.

## Evaluation

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.

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:

- 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

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.