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

1. Load the model predictions and ground truth dataset using the Read tool; verify schema includes sensitive attribute columns
2. Define the protected attributes and privileged/unprivileged group definitions for the fairness analysis
3. Compute representation statistics: group counts, class label distributions, and feature coverage per demographic segment