This is basically two questions stitched together and I didn't pace myself well.
Start by outlining offline evaluation using a labeled validation set, focusing on precision/recall trade-offs and threshold selection. Then design an online A/B test with clear hypotheses, variants, success metrics, and guardrails to measure real-world impact. Emphasize the importance of aligning offline metrics with online outcomes and iterating based on results.
Pro tip: Highlight the need to consider both user experience and fairness/bias implications, and propose a multi-metric evaluation framework that balances harm reduction with false positives. Show awareness of Meta's scale by discussing how to handle large-scale data and potential feedback loops.
Use a labeled validation set to compute precision, recall, F1, and AUC. Analyze performance across different content types and user segments to identify biases.
Choose an operating threshold based on the desired balance between false positives (over-removal) and false negatives (harmful content left up). Consider business and ethical implications.
Define a hypothesis (e.g., new model reduces harmful content prevalence without increasing false positives). Randomize users into control (current model) and treatment (new model) groups.
Primary metrics: prevalence of harmful content, user reports, and engagement. Guardrails: false positive rate, user satisfaction, and fairness metrics across demographics.
Analyze results for statistical significance and practical impact. If successful, consider gradual rollout; if not, diagnose issues and iterate on model or threshold.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.