How does model explainability trade off with bias and variance in regulated ML systems?

More explainable models often have higher bias but lower variance, while complex models are less interpretable but more flexible.

Explainability constraints often force model simplicity, which increases bias but reduces variance. Linear models, decision trees, and rule-based systems are highly interpretable but may underfit complex data. Deep learning models reduce bias significantly but are harder to interpret, increasing reliance on post-hoc explainability techniques like SHAP or LIME.

In regulated domains such as finance and healthcare, explainability is mandatory, so architects often accept higher bias to maintain compliance.…