This question sprawls in ways that are easy to underestimate.
Structure your answer around the core components of an offline evaluation framework: metrics, class imbalance, ground truth, threshold selection, and fairness. Emphasize that the framework must align with business objectives and operational constraints, and discuss trade-offs explicitly.
Pro tip: Anchor your answer in the business context: harm classifiers often prioritize recall for severe harms, and thresholds should be set based on cost ratios. Mention that offline metrics are proxies and should be validated with online experiments.
Clarify the goal: minimize harmful content while controlling false positives. Choose metrics like precision, recall, F1, PR-AUC, and per-class recall, considering the cost of different error types.
Use techniques like stratified sampling, class weights, or synthetic data to handle imbalance. Evaluate with metrics robust to imbalance, such as PR-AUC and balanced accuracy.
Leverage human reviewers with clear guidelines and quality checks (e.g., multiple annotators, adjudication). Consider active learning to prioritize uncertain samples and ensure representative sampling.
Model the business costs of false positives and false negatives. Use cost-sensitive analysis to pick a threshold that minimizes expected cost, and validate with a holdout set.
Evaluate performance across demographic groups and content types. Check for disparate impact, equal opportunity, and other fairness metrics, and mitigate biases if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.