Question

Your team deployed a resume-screening model to rank candidates for engineering roles. During a responsible AI review, you discover that although gender was explicitly removed from the training data, the model still recommends male candidates at a significantly higher rate. Investigation shows the model relies heavily on features like 'years of continuous employment' and membership in certain college sports clubs, which correlate strongly with gender. What is the MOST appropriate action to address this fairness risk?

Accepted Answer

Identify and mitigate the proxy features driving disparate impact, then evaluate the model against fairness metrics such as demographic parity or equalized odds before redeploying.

Answer

Re-add gender as a feature so the model can be explicitly calibrated to produce equal selection rates across groups.

Answer

Increase the training dataset size so the model learns a more general representation and the correlation disappears naturally.

Answer

Lower the decision threshold uniformly for all candidates to increase the number of people recommended and reduce overall disparity.