Weak supervision framework for learning to label concept explanations on tabular data

Abstract

In various embodiments, a process includes receiving input records including tabular data, where the input records are unlabeled for a concept-explainability task. The process includes obtaining primitives for at least a subset of the input records, where the obtained primitives are based at least on at least one annotation including a plurality of user-defined concept labels. The process includes training, using hardware processor(s), a plurality of candidate models using the obtained primitives. For each of the plurality of user-defined concept labels, at least one corresponding model from the plurality of candidate models is used to determine a corresponding concept labeling model. The process includes using the determined corresponding concept labeling models to label the input records with which to train a concept-explainability machine learning model using the labeled input records.