AI-assisted human data augmentation and continuous training for machine learning models

A method for training at least one classifier model used by an artificial intelligence (AI) system to recognize each of a set of objects and to assign each of the set of objects to a class, the method comprising:

• training the at least one classifier model on a training dataset, thereby producing at least one trained classifier model;
• using the at least one trained classifier model to detect and classify each member of a set of objects, thereby generating a set of inferences, wherein each inference includes
• (a) a cropped image of a classified object,
• (b) the classified object's inferred classification, and
• (c) a confidence score associated with the inferred classification;
• examining the set of inferences with a machine implemented audit trigger, wherein the audit trigger identifies a subset of the set of inferences whose members have (i) a confidence score that falls below a predetermined threshold value, or (ii) a missing inferred classification;
• if the identified subset has at least one member, subjecting the identified subset to a human audit, thereby yielding a corrected set of observations, wherein, for each member of the corrected set of observations, the inferred classification of the corresponding member of the set of inferences is replaced with a corrected classification;
• examining the corrected set of observations with a benefit scoring process, thereby identifying at least one proposed modification to the training dataset;
• and
• incorporating the at least one proposed modification into the training dataset in a subsequent iteration of the method.