Human-in-the-loop evaluation for explainable artificial intelligence

Abstract

A data stream is received. Data elements of the data stream are analyzed using one or more machine learning models and one or more machine learning prediction explanation implementations. Different candidate presentations are tested. The different candidate presentations are associated with machine learning results provided to different reviewers in a group of human-in-the-loop reviewers that review predictions of the one or more machine learning models. The different candidate presentations include different explanations generated by the one or more machine learning prediction explanation implementations and at least one control candidate presentation corresponding to an absent explanation. Different aspects of the testing are monitored. Results of the monitoring are used to make a selection among the different candidate presentations.