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Design a Content Moderation System

Build a large-scale content moderation system processing 10M+ posts daily with 99.5% accuracy and sub-second response times for real-time content.

Content SafetyMulti-modal AITrust & Safety
Q: What types of content and platforms need moderation?
A: Social media posts, comments, images, videos, livestreams across multiple platforms. Think Facebook-scale: 3B posts/day, covering hate speech, misinformation, violence, adult content, and spam.
Engineering Implications: Multi-modal content requires different AI models for each type. Real-time livestreams need sub-second processing. Scale demands distributed architecture with edge processing for global latency.
Q: What's the expected volume and processing requirements?
A: 10M posts/day baseline, 100M during events. Real-time moderation for live content ({'<'}1s), batch processing acceptable for historical analysis. Support 50+ languages and cultural contexts.
Engineering Implications: Massive scale requires auto-scaling infrastructure, efficient model serving, and language-specific fine-tuning. Cultural context needs regional policy variations and local human reviewers.
Q: What accuracy and safety requirements do we have?
A: 99.5% accuracy for policy violations, {'<'}0.1% false positives on legitimate content. High precision critical - false negatives hurt users, false positives hurt creators and platform trust.
Engineering Implications: Requires ensemble models, human-in-the-loop validation, and sophisticated confidence scoring. Appeals process needs rapid human review within 24h for creator economy.
Q: What about regulatory and compliance requirements?
A: GDPR, DSA (Digital Services Act), local regulations like NetzDG. Transparency reporting, data retention policies, user rights (appeal, explanation, data portability).
Engineering Implications: Compliance requires audit trails, explainable AI decisions, data anonymization, and regional data residency. Legal frameworks vary by jurisdiction.
Q: How should the human review process work?
A: Tier 1: Community reporting, Tier 2: ML confidence <0.8 goes to human review, Tier 3: Appeals and complex cases. Need 24/7 coverage across time zones with specialist reviewers.
Engineering Implications: Human workforce management with training, quality assurance, and mental health support. Workflow tools for consistent decisions and reviewer productivity.

Complete Content Moderation ML Systems Framework

🎯 Section 3: ML Task Framing

  • • Multi-class classification: hate speech, violence, spam, adult content
  • • Multi-modal models: text (BERT), vision (ResNet), video (3D CNN)
  • • Ensemble approach with confidence calibration
  • • Success metrics: 99.5% accuracy, <0.1% false positive rate

📊 Section 4: Data Preparation

  • • Training data: 100M labeled examples across 50 languages
  • • Human annotation with inter-annotator agreement >0.8
  • • Synthetic data generation for rare policy violations
  • • Privacy-preserving techniques: differential privacy, federated learning

🧠 Section 5: Model Architecture

  • • Text: RoBERTa-large fine-tuned per language (96% accuracy)
  • • Vision: EfficientNet-B7 for image classification (94% accuracy)
  • • Video: I3D for temporal content analysis (91% accuracy)
  • • Ensemble: Weighted voting with uncertainty quantification

🚀 Section 6: Training Pipeline

  • • Stage 1: Pre-training on general web data (1 month)
  • • Stage 2: Domain adaptation on platform-specific data
  • • Stage 3: Policy-specific fine-tuning with human feedback
  • • Continuous learning: Daily retraining on new violations

📈 Section 7: Evaluation Framework

  • • Offline: Precision/Recall per policy category, AUC-ROC
  • • Online: A/B testing with user impact metrics
  • • Adversarial testing: Red team attacks and edge cases
  • • Fairness audits: Bias detection across demographics

🏗️ Section 8: Production Architecture

  • • Microservices: Content ingestion, ML inference, policy engine
  • • Real-time stream processing with Apache Kafka
  • • Multi-region deployment with <100ms global latency
  • • Human review tools with workflow management

⚖️ Key Trade-offs & Decisions

  • • Precision vs Recall: Optimized for user safety over creator convenience
  • • Real-time vs Batch: 20% real-time for live content, 80% batch processing
  • • Global vs Local: Regional models for cultural context compliance
  • • Automation vs Human: 95% automated, 5% human review for edge cases

🔧 Implementation Challenges

  • • Adversarial attacks: Content designed to evade detection
  • • Context dependency: Sarcasm, cultural references, evolving slang
  • • Scale challenges: Viral content spikes, model deployment
  • • Regulatory compliance: GDPR, DSA, regional law variations
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🎯 Interview Practice Questions

Practice these follow-up questions to demonstrate deep understanding of content moderation systems in interviews.

1. Multi-Modal Hate Speech Detection

"Your content moderation system needs to detect hate speech in memes (image + text combinations). How do you design a solution that understands context between visual and textual elements, and how do you handle cases where neither element alone is problematic but together they violate policies?"

2. Adversarial Content Defense

"Bad actors are using techniques like character substitution, mirrored text, and embedded text in images to evade your moderation. Design a robust defense system that can detect these evasion techniques while maintaining low false positive rates on legitimate creative content."

3. Cultural Context Scaling

"Your platform is expanding to 20 new countries with different cultural norms around acceptable content. How do you adapt your moderation system to handle regional policy variations while maintaining consistent global brand safety standards?"

4. Human-AI Collaboration Workflow

"Design a workflow system where human moderators can efficiently review edge cases flagged by AI. Include considerations for reviewer fatigue, consistency across shifts, training for new policy updates, and maintaining quality while scaling to 1000+ reviewers globally."

5. Real-time Viral Content Re-evaluation

"A piece of content is going viral (1M shares in 30 minutes) but your system initially classified it as borderline acceptable. How do you design a re-evaluation system that can quickly reassess viral content for potential harm while minimizing disruption to legitimate viral trends?"

6. Regulatory Compliance Architecture

"Your content moderation system must comply with the EU's Digital Services Act, which requires transparency reporting, user appeal rights, and risk assessments. Design the architecture changes needed to support audit trails, explainable AI decisions, and user data rights while maintaining system performance."