This is the kind of question where you think you know where to start and then realize halfway through that you've already gone down a rabbit hole.
Start by clarifying requirements and scope, then outline a high-level system architecture that combines rule-based filters, ML classifiers, and human review. Emphasize trade-offs between precision and recall, scalability, and adversarial robustness, and discuss how to measure success and iterate.
Pro tip: Show awareness of adversarial evasion and the need for a multi-layered approach; mention that no single model is sufficient and that continuous monitoring and retraining are essential.
Ask questions to understand what constitutes a 'weapon sale', platform policies, scale, latency needs, and acceptable false positive/negative rates.
Identify data sources (posts, images, messages, user reports) and discuss labeling strategies, including active learning and handling class imbalance.
Propose a multi-modal system: text classifiers (e.g., BERT), image detection (e.g., object detection), and graph-based signals; combine with rules and anomaly detection.
Design a pipeline for real-time and batch processing, including feature extraction, model serving, and integration with human review queues.
Define metrics (precision, recall, F1, AUC), set up A/B testing, and plan for adversarial adaptation and continuous retraining.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.