Question

A bank uses a machine learning model to approve loan applications. During review, the responsible-AI team finds the model achieves very high overall accuracy but approves applicants from one demographic group at a substantially higher rate than equally qualified applicants from another group. Leadership wants the model to treat qualified applicants equitably, even if it means the top-line accuracy number decreases slightly. Which responsible-AI dimension and trade-off does this situation primarily illustrate?

Accepted Answer

Prioritizing fairness may require accepting a small reduction in overall accuracy to reduce disparate outcomes across groups

Answer

Prioritizing robustness requires retraining the model on adversarial examples to prevent accuracy loss

Answer

Prioritizing transparency requires publishing the model's source code so accuracy can be independently verified

Answer

Prioritizing explainability requires generating feature attributions so accuracy always improves over time