Editing AI Governance and Policy (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Conducting a bias audit with Fairlearn:'''
<syntaxhighlight lang="python">
from fairlearn.metrics import MetricFrame, demographic_parity_difference, equalized_odds_difference
from sklearn.metrics import accuracy_score, precision_score, recall_score
import pandas as pd

# Predictions and sensitive feature (e.g., race, gender)
y_true = test_labels
y_pred = model.predict(X_test)
sensitive_feature = test_df['race']  # or 'gender', 'age_group'

# Comprehensive fairness audit
metrics = {
    'accuracy': accuracy_score,
    'precision': precision_score,
    'recall': recall_score,
}
mf = MetricFrame(
    metrics=metrics,
    y_true=y_true,
    y_pred=y_pred,
    sensitive_features=sensitive_feature
)

print("Overall metrics:")
print(mf.overall)
print("\nMetrics by group:")
print(mf.by_group)
print(f"\nDemographic parity difference (approval rate gap): {demographic_parity_difference(y_true, y_pred, sensitive_features=sensitive_feature):.3f}")
print(f"Equalized odds difference (FPR + FNR gap): {equalized_odds_difference(y_true, y_pred, sensitive_features=sensitive_feature):.3f}")
# Target: |demographic parity difference| < 0.05 for most applications
# Report and document all results for compliance records
</syntaxhighlight>

; AI governance by jurisdiction
: '''EU (comprehensive)''' → EU AI Act (binding); GDPR Art. 22 (automated decisions)
: '''US (sector-specific)''' → FTC Act, ECOA, FCRA, FDA guidance, NIST AI RMF
: '''UK''' → AI Safety Institute, sector-by-sector approach; AI Act alignment pending
: '''China''' → Algorithmic recommendation rules, generative AI regulations, PIPL
: '''International''' → OECD AI Principles, UNESCO Recommendation on AI, G7 Hiroshima AI Code
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