Explainable Machine Learning
Machine learning models designed to provide clear and understandable explanations for their predictions and decisions.
Definition
Involves choosing inherently interpretable algorithms (decision trees, rule lists) or building hybrid models that balance accuracy and transparency. Governance best practices include documenting model logic, user-testing explanation clarity, and restricting opaque models to low-risk applications when explainable alternatives exist.
Real-World Example
A mortgage lender uses an explainable decision-tree model for initial loan approvals. Each decision path is translated into plain-language rules (e.g., “If income > $50K and credit score > 700, approve”), enabling loan officers and auditors to trace every approval directly to human-readable criteria.
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