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 estimates

• Compliance matrix: pass/fail against IEEE, EU, and ACM ethical guidelines with evidence citations

Error Handling

Error Cause Solution

Insufficient group sample size Fewer than 30 observations in a demographic group Aggregate related subgroups; use bootstrap confidence intervals; flag metric as unreliable

Missing sensitive attributes Protected attribute columns absent from dataset Apply proxy detection via correlated features; request attribute access under data governance approval

Conflicting fairness criteria Demographic parity and equalized odds contradict Document the impossibility theorem trade-off; prioritize the metric most aligned with the deployment context

Data quality failures Inconsistent encoding or null values in attribute columns Standardize categorical encodings; impute or exclude nulls; validate with schema checks before analysis

Model output format mismatch Predictions not in expected probability or binary format Convert logits to probabilities via sigmoid; binarize at the decision threshold before metric computation

Examples

Scenario 1: Hiring Model Audit -- Validate a resume-screening classifier for gender and age bias. Compute demographic parity across male/female groups and age buckets (18-30, 31-50, 51+). Apply the four-fifths rule. Finding: female selection rate at 0.72 of male rate (critical severity). Recommend reweighting training samples and adjusting the decision threshold.

Scenario 2: Credit Scoring Fairness -- Assess a credit approval model for racial disparate impact. Calculate equalized odds (TPR and FPR) across racial groups. Finding: FPR for Group A is 2.1x Group B (high severity). Recommend in-processing constraint using ExponentiatedGradient with FalsePositiveRateParity .

Scenario 3: Healthcare Risk Prediction -- Evaluate a patient risk model for age and socioeconomic bias. Compute calibration curves per group. Finding: model overestimates risk for low-income patients by 15%. Recommend recalibration using Platt scaling per subgroup with post-deployment monitoring for fairness drift.

Resources

• Fairlearn Documentation -- bias detection, mitigation algorithms, MetricFrame API

• AI Fairness 360 (AIF360) -- comprehensive fairness toolkit with 70+ metrics

• Google What-If Tool -- interactive fairness exploration

• EU Ethics Guidelines for Trustworthy AI -- regulatory framework

• IEEE Ethically Aligned Design -- technical ethics standards

• Impossibility theorem reference: Chouldechova (2017) on incompatibility of fairness criteria