Content Moderation

Content moderation aims to identify material such as hate speech, harassment, self-harm, and unsafe images or videos, then guide decisions about removal, review, or rewriting. Approaches range from text classifiers and multimodal vision-language models to policy-guided language models using retrieval, examples, or step-by-step reasoning; some systems also screen content as it is generated or streamed. Researchers evaluate these methods on labeled datasets and policy-specific benchmarks, including tests of multilingual content, memes, evolving rules, and adversarial changes to text or images. A central tension is that models must apply context-dependent policies consistently while avoiding unfair errors, resisting evasion, and meeting the cost and latency limits of real moderation workflows.