This is the kind of question where you can spiral fast if you don't anchor early.
Start by clarifying the scope: define what constitutes an advertisement selling weapons, including policy nuances (e.g., legal vs. illegal weapons, context like toys or video games). Then outline an end-to-end ML system covering data collection, labeling, feature engineering, model selection, deployment, and monitoring, while addressing trade-offs like precision vs. recall and scalability.
Pro tip: Emphasize a human-in-the-loop approach and multi-modal signals (text, image, video) to improve accuracy and adapt to evolving adversarial tactics, showing you understand real-world content moderation challenges at scale.
Ask questions to define what 'advertisements selling weapons' means: types of weapons (firearms, explosives, etc.), legal vs. illegal, and platforms (feed, marketplace, ads). Establish success metrics like precision, recall, and latency.
Identify data sources: ad text, images, videos, landing pages, and user reports. Design a labeling schema with policy experts and use techniques like active learning and weak supervision to scale labeling.
Extract multi-modal features: text (keywords, embeddings), image (object detection, OCR), and metadata. Choose models like fine-tuned transformers for text, CNNs for images, and ensemble or multi-modal fusion for combined signals.
Train models with class imbalance handling (e.g., focal loss). Evaluate using precision-recall curves, and set thresholds based on business cost of false positives vs. false negatives. Consider adversarial robustness and bias mitigation.
Deploy as a real-time service with a human review queue for borderline cases. Monitor performance drift, adversarial attacks, and feedback loops. Continuously retrain with new data and update policies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.