ADJUSTING A CLASSIFICATION MODEL BASED ON ADVERSARIAL PREDICTIONS

摘要

This application addresses techniques to de-correlate classifiers (e.g., render them neutral) to certain target groups. Classifiers can, for example, determine the intent of content (e.g., shopping, news, etc.), flag target content, etc. Sometimes, these classification categories may be incorrectly associated with certain types, groups, characteristics, etc. Exemplary embodiments retrain a classifier's model in an adversarial manner to render it no better than chance at detecting whether content originated from an entity embodying a target type, group, characteristic, etc.