12-24-2025, 01:06 PM
I'm a data scientist working on a credit risk model, and while our new ensemble algorithm achieves excellent accuracy, the business stakeholders and compliance officers are rightfully demanding Explainable AI because the "black box" nature of the model makes it impossible to justify individual loan denials or satisfy regulatory requirements. We're exploring techniques like SHAP and LIME, but I'm finding that the explanations can be unstable or too complex for non-technical audiences to trust. For others who have implemented XAI in a regulated industry, how did you bridge the gap between technically sound explanations and actionable, auditable insights for business users? What frameworks or documentation practices did you adopt to make the model's decision-making process transparent and defensible, and how did you manage the inevitable trade-off between model performance and interpretability when choosing your final approach?