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SHAP for feature attribution
SHAP quantifies each feature’s contribution to a model prediction, enabling:
- Root-cause analysis
- Bias detection
- Detailed anomaly interpretation
LIME for local interpretability
LIME builds simple local models around a prediction to show how small changes influence outcomes. It answers questions like:
- “Would correcting age change the anomaly score?”
- “Would adjusting the ZIP code affect classification?”
Explainability makes AI-based data remediation acceptable in regulated industries.
More reliable systems, less human intervention
AI augmented data quality engineering transforms traditional manual checks into intelligent, automated workflows. By integrating semantic inference, ontology alignment, generative models, anomaly detection frameworks and dynamic trust scoring, organizations create systems that are more reliable, less dependent on human intervention, and better aligned with operational and analytics needs. This evolution is essential for the next generation of data-driven enterprises.
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