Black Box to Glass Box: Explainable AI Is Ending Blind Attrition Scores

For years, people analytics teams could predict which employees were likely to leave — but could not explain why. A manager would receive a flight-risk score of 0.78 with no context. New explainable AI techniques like SHAP and LIME are changing that completely, delivering transparent, per-employee breakdowns behind every attrition prediction. The accuracy is there too. A 2026 study in Frontiers in Big Data hit 97.37% AUC-ROC using explainable AI methods — putting to rest the argument that transparency comes at the cost of performance. And with the EU AI Act set to classify attrition prediction as high-risk AI, the glass box is becoming a regulatory requirement, not just a best practice. This episode breaks down how SHAP and LIME work in plain terms, why explainability changes intervention strategies, and what your people analytics team should be asking vendors right now.