Question

A bank is building a loan-approval model. Regulators require that the bank be able to explain to each rejected applicant exactly which factors drove the decision. The data science team is choosing between a highly accurate deep neural network and a simpler, inherently interpretable model. Which consideration should most influence their choice for this use case?

Accepted Answer

An interpretable model may be preferable because the regulatory requirement for clear per-decision explanations can outweigh a marginal accuracy gain.

Answer

The deep neural network should be selected because higher accuracy always outweighs any need for explanation.

Answer

Model choice is irrelevant because all machine learning models are equally explainable by default.

Answer

The team should pick whichever model trains fastest, since explainability is only a concern during training, not deployment.