Responsible AI and Governance

Responsible AI and AI Governance

# AI Governance Framework

# 1. Bias Detection and Mitigation
from fairlearn.metrics import MetricFrame, selection_rate
from sklearn.metrics import accuracy_score

metric_frame = MetricFrame(
    metrics={"accuracy": accuracy_score,
             "selection_rate": selection_rate},
    y_true=y_test,
    y_pred=y_pred,
    sensitive_features=gender_feature  # protected attribute
)
print(metric_frame.by_group)  # accuracy per demographic group
# Flag if disparity > acceptable threshold

# 2. Model Explainability (XAI)
import shap

explainer = shap.Explainer(model)
shap_values = explainer(X_test)
shap.plots.beeswarm(shap_values)  # feature importance
shap.plots.waterfall(shap_values[0])  # single prediction explanation

# 3. AI Audit Log
log.info("ai_decision",
         model_id="credit-scorer-v3",
         input_hash=hash(str(features)),
         decision="approved",
         confidence=0.87,
         top_features=shap_top_3,
         user_id=applicant_id)

# 4. EU AI Act compliance
# High-risk systems: credit scoring, hiring, medical, law enforcement
# Requirements: human oversight, explainability, bias testing
# NIST AI RMF: Govern, Map, Measure, Manage