Training and using machine learning models to provide counterfactual explanations of predictions

Abstract

Provided are techniques for training and using machine learning models to provide counterfactual explanations of predictions. An Artificial Intelligence (AI) predictive model is trained. The AI predictive model is used to generate a prediction label for each item of a plurality of input items. A target item with an initial prediction label. For a morphological segment, a source item is identified from the plurality of input items, where the source item shares common structural features with the target item and has a different prediction label. A recombined item is generated by: masking the morphological segment in the target item and adding the morphological segment of the source item. The AI predictive model is used to generate a new prediction label for the recombined item. It is determined that the new prediction label is different from the initial prediction label and that the recombined item is a counterfactual item.