"Design a content moderation system for a short video platform that processes millions of uploads daily, balancing speed, accuracy, and cost."
Content safety is a top priority at ByteDance due to regulatory requirements across markets. This question evaluates your ability to design ML pipelines with human-in-the-loop workflows, handle multi-modal content (video, audio, text), and manage the trade-off between automated efficiency and human accuracy.
Outline the multi-stage pipeline: automated pre-screening at upload, ML-based classification, human review queue, and post-publish monitoring.
Describe the ML models involved: video frame classification (NSFW, violence), audio speech recognition and analysis, OCR for text overlay, and multimodal fusion.
Explain the decision framework: confidence thresholds for auto-approve, auto-reject, and human review escalation, with different thresholds for different violation categories.
Address scalability and feedback loops: how human review labels improve ML models, active learning for edge cases, and regional policy customization.
I would design a multi-layered moderation pipeline. At upload time, lightweight models perform pre-screening: perceptual hashing for known-bad content matching, and fast classifiers for obvious violations to block immediately. Content passing pre-screening enters the main ML pipeline: video frames are sampled and classified using CNN models for categories like NSFW, violence, and dangerous activities; audio is transcribed and analyzed for hate speech; OCR extracts text overlays for policy violations. A fusion model combines all signals with confidence scores. Content scoring above 0.95 violation confidence is auto-rejected; below 0.3 is auto-approved; everything between enters a prioritized human review queue. Human reviewers use specialized tooling with region-specific policy guidelines. Their decisions feed back into model training via active learning, prioritizing edge cases near decision boundaries. Post-publish monitoring continuously re-evaluates content based on user reports and updated models, catching evolving policy violations.
Emphasize the multi-modal nature—ByteDance videos contain visual, audio, and text content that all need moderation.
Discuss regional policy differences, as ByteDance operates under different regulations in China, the US, EU, and Southeast Asia.
Mention the cost-accuracy trade-off: human review is expensive, so optimizing the auto-decision rate is crucial.
Use adversarial training to make models robust, deploy honeypot detection for accounts repeatedly testing boundaries, implement multi-round review where flagged accounts get stricter thresholds, and continuously update models with newly discovered evasion techniques.
Key metrics include precision and recall per violation category, auto-decision rate (percentage not requiring human review), time-to-action for violations, false positive rate affecting creator experience, and human reviewer agreement rates.
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