The company also employs a Pin model trained using a mathematical, model-friendly representation of Pins based on their keywords and images, aggregated with another model to generate scores that indicate which Pinterest boards might be in violation. When enforcing policies across Pins, the platform groups together Pins with similar images and identifies them by a unique hash called "image-signature." Models generate scores for each image-signature, and based on these scores, the same content moderation decision is applied to all Pins with the same image-signature. Since users usually save thematically related Pins together as a collection on boards around topics like recipes, Pinterest deployed a machine learning model to produce scores for boards and enforce board-level moderation. A Pin model trained using only embeddings -- i.e., representations -- generates content safety scores for each Pinterest board.