Design a harmful content detection system

Design an end-to-end harmful content detection system. Define the taxonomy (e.g., hate, self-harm, sexual content, violence), labeling guidelines and quality controls, and multilingual/multimodal scope (text, image, audio, video). Propose model choices (keyword baselines, classical ML, transformers, multimodal encoders) and training data strategy (collection, active learning, long-tail sampling, debiasing). Specify inference architecture (streaming vs. batch), thresholds and severity tiers, human-in-the-loop review, appeals/override flows, and explainability requirements. Address adversarial behavior (evasion, prompt injection), privacy and safety constraints, fairness and error costs (precision/recall trade-offs by class and region), monitoring and drift detection, A/B rollout, and feedback loops for continuous improvement.