Abstract: Methods and systems for generating a counterfactual explanation with respect to a prediction are provided. The method includes: receiving a set of data items that relate to respective characteristics of a situation; generating a first prediction that corresponds to an undesirable outcome with respect to the situation based on the set of data items; generating a second prediction that corresponds to a desirable outcome with respect to the situation; and determining a counterfactual explanation that indicates a potential change to at least one data item included in the set of data items such that the potential change corresponds to the second prediction. The determination may be made by applying an artificial intelligence (AI) algorithm that uses a machine learning technique to analyze each respective data item included in the set of data items.

Abstract: A method for providing a global counterfactual explanation and a system for implementing the method are disclosed. The method includes generating an initial ground set based on a first candidate set of outer-If conditions, and a second candidate set used for selecting Inner-If or Then conditions. The method then evaluates a fixed number of triples and forms a new ground set that provides a recourse accuracy level above a reference threshold, in which the fixed number of triples included in the new ground set is less than a number of triples included in the initial ground set. The method further includes sorting the new ground set by recourse accuracy, selecting a predetermined number of triples based on corresponding recourse accuracies indicated in the sorting, and performing calculation based on the selected number of triples.