A data science team notices that a credit-scoring model approves loans at a significantly lower rate for one demographic group compared to all others, even though that group has similar repayment histories. Which practice most directly addresses this disparity during model development?
- AEvaluating disaggregated fairness metrics across demographic subgroups and retraining with corrected data or constraints Correct
- BIncreasing the model's overall accuracy on the full training dataset
- CDeploying the model behind an API so that end users cannot inspect its decision logic
- DRemoving all demographic attributes from the training features before the initial model fit
Why A is correct: Disaggregated evaluation exposes differential performance per subgroup, allowing targeted mitigation such as re-sampling, re-weighting, or fairness constraints during retraining.
Why B is wrong: Tempting because higher accuracy sounds like a better model, but aggregate accuracy can improve while bias against a subgroup worsens if that group is a small fraction of the dataset.
Why C is wrong: Hiding decision logic via an API does nothing to correct the underlying bias; it only reduces transparency, which compounds responsible AI concerns.
Why D is wrong: Removing protected attributes is a common first step but does not eliminate bias because correlated proxy features (postcode, occupation) can still encode demographic information.