Zero-Shot Content Moderation with OpenAI's New CLIP Model

Content moderation is a complex but essential task for platforms that host user-generated content, as demonstrated by the example of Facebook's efforts to combat vaccine misinformation. Traditional moderation methods include human, non-model-based, and model-based approaches, each with unique advantages and challenges. While human moderation offers tailored oversight, it struggles with scalability, whereas automated methods can process large volumes of content but may inherit biases from training data. Enter OpenAI's CLIP model, which offers a novel "zero-shot" approach by understanding the semantic meaning of images without needing a pre-curated dataset. This capability was leveraged in the game paint.wtf, where CLIP evaluates drawings based on their alignment with given prompts using cosine similarity. The model can also identify inappropriate content, such as NSFW images, by comparing them against specific categories, thus reducing the burden of manual moderation and enhancing user experience. This innovative use of CLIP demonstrates its versatility in content moderation across unique domains, underscoring its potential to reshape how platforms manage user contributions.