Explainable AI (XAI)

Explainable AI (XAI) refers to methods that make an AI system's outputs understandable to people: which inputs drove a prediction, which rules fired, and why a given action was taken. In HR it is what lets a manager see why an employee was flagged as an attrition risk, or an auditor reconstruct why an agent approved a request.

For predictive models, explainability uses feature-importance techniques (SHAP and LIME are the common ones) or favors interpretable model types in high-stakes decisions. For agents, it means exposing reasoning steps and source inputs alongside the answer. The distinction that matters for enterprise buyers is between a log and a decision record: a log says what ran; a decision record says why. Darwinbox uses SHAP and LIME for model explanations and surfaces agent reasoning and inputs for audit.