Start by clarifying requirements and scale, then propose a multi-stage pipeline that combines fast automated classifiers for upload-time filtering with periodic batch scanning for retroactive detection. Emphasize human-in-the-loop review for ambiguous cases, and design feedback loops to continuously improve model accuracy while minimizing false positives/negatives. Finally, outline the appeals and audit trail systems to ensure transparency and accountability.
Pro tip: Quantify trade-offs: e.g., discuss how adjusting classification thresholds affects precision/recall and the resulting human review load, showing you understand the operational costs and can make data-driven decisions.
Ask about platform scale (uploads per second, total videos), violation categories, latency requirements, and accuracy targets. This sets the stage for designing a system that balances performance and cost.
Propose a pipeline: fast hash-matching for known violations, lightweight ML models for immediate triage, and heavier models for deeper analysis. Include both upload-time (synchronous) and retroactive (asynchronous batch) scanning.
Route low-confidence predictions to human moderators, and use their decisions to retrain models. Implement active learning to prioritize uncertain cases for labeling, improving efficiency.
Discuss threshold tuning, ensemble methods, and category-specific models. Consider business impact: false positives harm creators, false negatives harm users. Use metrics like precision/recall and cost-sensitive learning.
Design a transparent appeals process where creators can contest decisions, with human re-review. Maintain immutable logs of all actions (automated and human) for auditing and compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.