Question 1
Domain 2: Evaluation, Tuning, and Quality OptimizationWhat bias detection and mitigation strategy would best address this issue?
Correct answer: B
Explanation
This strategy addresses bias end-to-end by measuring it with fairness metrics like "demographic parity" and "equal opportunity," then reducing it through "adversarial debiasing" and "fairness constraints" during training. Ongoing "continuous bias monitoring in production" and "regular bias audits with diverse test sets" catch drift and hidden disparities after deployment.
Why each option is right or wrong
A. Remove gender information from resumes before processing.
B. Implement comprehensive bias mitigation with: bias metrics measurement (demographic parity, equal opportunity), adversarial debiasing during training, fairness constraints in recommendation algorithm, continuous bias monitoring in production, and regular bias audits with diverse test sets.
The proposed approach is the only one that covers the full lifecycle of bias control: it uses measurable fairness criteria such as demographic parity and equal opportunity to quantify disparate outcomes, then applies training-time controls like adversarial debiasing and explicit fairness constraints to reduce those disparities before deployment. The inclusion of continuous production monitoring and periodic audits with diverse test sets is critical because bias can reappear after release due to data drift, user feedback loops, or subgroup underrepresentation; without those post-deployment checks, the mitigation would be incomplete.
C. Manually review all recommendations to filter biased decisions.
D. Train the model on a perfectly balanced dataset with equal gender representation.