Content Moderation & Fraud Detection - Patterns in Industry
Eugene Yan
Content moderation and fraud detection systems rely on five key patterns: collecting ground truth through user flags and human annotation, augmenting initial labeled data with synthetic examples, cascading simple heuristics before applying machine learning models, combining supervised and unsupervised approaches, and using explainability to understand model decisions. Companies like DoorDash achieved a 100:1 ratio of synthetic to actual training labels for menu item tagging, while Cloudflare's heuristics classify 15-30% of traffic at 20ms with lower false positive rates than machine learning alone. These patterns enable organizations to build robust systems that start with cheap rule-based filtering upstream, reserving machine learning for harder cases downstream where heuristics lack confidence.
Why it matters
Collecting ground truth, data augmentation, cascading heuristics and models, and more.
