Abstract: Systems and methods for monitoring the quality of document reviews used in continuous active machine learning are described herein. Two orthogonal processes may be run simultaneously, asynchronously, and continuously. The first process performs continuous active machine learning for training machine classification models. The second process classifies documents that have been reviewed as part of the first process to generate classification scores of the reviewed documents. The original review may be compared to the classification scores using false negative and a false positive thresholds to identify documents that may have been incorrectly reviewed. A master review of identified documents is used to correct original reviews that were incorrect. Original incorrect reviews may be replaced in a training corpus by corrected reviews, and the models may be retrained using the corrected reviews.

Abstract: Systems and methods for monitoring the quality of document reviews used in continuous active machine learning are described herein. Two orthogonal processes may be run simultaneously, asynchronously, and continuously. The first process performs continuous active machine learning for training machine classification models. The second process classifies documents that have been reviewed as part of the first process to generate classification scores of the reviewed documents. The original review may be compared to the classification scores using false negative and a false positive thresholds to identify documents that may have been incorrectly reviewed. A master review of identified documents is used to correct original reviews that were incorrect. Original incorrect reviews may be replaced in a training corpus by corrected reviews, and the models may be retrained using the corrected reviews.