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FIDDLER
Enterprise AI observability with explainability and fairness
ABOUT
Regulated industries need more than model accuracy — they need to explain every prediction, detect bias across demographic groups, and prove compliance with fairness standards. Most monitoring tools only track performance metrics without providing interpretability. Fiddler combines model monitoring with explainable AI and fairness auditing in one platform. It surfaces which features drove each prediction (SHAP, LIME), detects bias across protected attributes, tracks performance drift over time, and generates compliance-ready reports for auditors and regulators.
INSTALL
pip install fiddler-clientINTEGRATION GUIDE
1. Generate SHAP-based explanations for every model prediction to satisfy regulatory compliance requirements
2. Detect and measure bias across demographic groups with automated fairness auditing and disparity alerts
3. Monitor model performance drift across segments with drill-down to feature-level root cause analysis
4. Build compliance-ready dashboards with point-in-time snapshots of model behavior for regulatory audits
5. Track LLM output safety and fairness with guardrails that flag biased or harmful generations
TAGS
monitoringobservabilityexplainable-aifairnessbias-detectionmlopspythonenterprisemodel-monitoring