Fairness, Bias Mitigation, and Responsible AI in Production Questions
Understand bias sources in ML systems and fairness metrics (demographic parity, equalized odds, calibration across groups). Design bias testing and monitoring. Discuss mitigation strategies: diverse data, algorithmic debiasing, and post-processing. For Staff-level, embed responsible AI practices into organizational processes.
EasyTechnical
64 practiced
Explain the differences between demographic parity, equalized odds, and calibration (group-wise calibration). For each metric: give a formal definition, a concise loan-approval example showing how it would be measured, and list the main strengths and weaknesses. Finally, state which metric you would prioritize if (a) regulators require equal treatment across groups, and (b) downstream decisions require well-calibrated risk scores.
MediumTechnical
57 practiced
Explain intersectional fairness and the combinatorial explosion of subgroup evaluations when considering multiple protected attributes. Propose scalable strategies to evaluate and prioritize subgroups for monitoring in production (e.g., hierarchical testing, risk-based prioritization, anomaly detection).
MediumTechnical
68 practiced
Discuss how imposing fairness constraints interacts with the classical bias-variance trade-off. Provide examples where enforcing fairness increases bias or variance, and propose practical strategies (regularization, ensembling, data augmentation) to manage these effects in real-world production models.
MediumTechnical
66 practiced
As a senior ML engineer, create a pre-production fairness checklist (gates) for model launch. Include dataset checks, bias and fairness metrics, edge-case tests, documentation (model card), privacy checks, and an approval workflow with responsible roles. Explain why each gate is important and suggest acceptable thresholds or criteria.
MediumTechnical
72 practiced
Implement a Python function that computes per-group thresholds to approximately equalize True Positive Rate (TPR) across two groups for probabilistic binary predictions. Inputs: y_score (probabilities), y_true, sensitive labels. Output: per-group thresholds. Describe whether you use grid search, ROC interpolation, or another approach, and handle ties/small groups.
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