Validate AI/ML models and datasets for bias, fairness, and ethical concerns.
ReadWriteEditGrepGlobBash(python:*)
AI Ethics Validator
Overview
Validate AI/ML models and datasets for bias, fairness, and ethical compliance using quantitative fairness metrics and structured audit workflows.
Prerequisites
- Python 3.9+ with Fairlearn >= 0.9 (
pip install fairlearn)
- IBM AI Fairness 360 toolkit (
pip install aif360) for comprehensive bias analysis
- pandas, NumPy, and scikit-learn for data manipulation and model evaluation
- Model predictions (probabilities or binary labels) and corresponding ground truth labels
- Demographic attribute columns (age, gender, race, etc.) accessible under appropriate data governance
- Optional: Google What-If Tool for interactive fairness exploration on TensorFlow models
Instructions
- Load the model predictions and ground truth dataset using the Read tool; verify schema includes sensitive attribute columns
- Define the protected attributes and privileged/unprivileged group definitions for the fairness analysis
- Compute representation statistics: group counts, class label distributions, and feature coverage per demographic segment
- Calculate core fairness metrics using Fairlearn or AIF360:
- Demographic parity ratio (selection rate parity across groups)
- Equalized odds difference (TPR and FPR parity)
- Equal opportunity difference (TPR parity only)
- Predictive parity (precision parity across groups)
- Calibration scores per group (predicted probability vs observed outcome)
- Apply four-fifths rule: flag any metric where the ratio falls below 0.80 as potential adverse impact
- Classify each finding by severity: low (ratio 0.90-1.0), medium (0.80-0.90), high (0.70-0.80), critical (below 0.70)
- Identify proxy variables by computing correlation between non-protected features and sensitive attributes
- Generate mitigation recommendations: resampling, reweighting, threshold adjustment, or in-processing constraints (e.g.,
ExponentiatedGradient from Fairlearn)
- Produce a compliance assessment mapping findings to IEEE Ethically Aligned Design, EU Ethics Guidelines for Trustworthy AI, and ACM Code of Ethics
- Document all ethical decisions, trade-offs, and residual risks in a structured audit report
Output
- Fairness metric dashboard: per-group values for demographic parity, equalized odds, equal opportunity, predictive parity, and calibration
- Severity-classified findings table: metric name, affected groups, ratio value, severity level, recommended action
- Representation analysis: group sizes, class distributions, feature coverage gaps
- Proxy variable report: features correlated with protected attributes above threshold (r > 0.3)
- Mitigation plan: ranked strategies with expected fairness improvement and accuracy trade-off
