Context-aware artificial intelligence system for content moderation

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing computing instructions configured to, when run on the one or more processors, cause the one or more processors to perform:
• determining, via a multi-channel text model, a combined text profanity score for textual content of an online promotion associated with an item, wherein the multi-channel text model comprises:
• a first channel text model that generates one or more text embeddings for the textual content and generates a first text profanity score for the textual content from the one or more text embeddings;
• a second channel text model that generates a respective text profanity degree for each of a plurality of profanity categories, wherein the respective text profanity degree is indicative of the textual content being classified into a respective profanity category, wherein the second channel text model is different from the first channel text model;
• and
• a meta-learner model that generates the combined text profanity score for the textual content by combining the first text profanity score with each of the respective text profanity degrees;
• determining, via a vision model, a vision profanity score for image content of the online promotion, wherein the vision model generates one or more vision tensors for the image content and determines the vision profanity score from the vision tensors;
• determining whether the combined text profanity score exceeds a text blocking score;
• determining whether the vision profanity score exceeds a vision blocking score;
• and
• upon determining that the combined text profanity score exceeds the text blocking score or that the vision profanity score exceeds the vision blocking score, setting a blocking label for the item in an item database as blocked, wherein the item is blocked or unblocked in the online promotion based on the blocking label.