Realistic counterfactual explanation of machine learning predictions

A system comprising:

• (i) at least one hardware processor;
• and
• (ii) a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by said at least one hardware processor to:
• automatically analyze a machine learning dataset comprising at least one datapoint to deduce constraints on features of the at least one datapoint;
• generate a first set of CSP (Constraint Satisfaction Problem) rules expressing the constraints;
• based on a machine learning model trained on the machine learning dataset, generate a second set of CSP rules that define one or more perturbation candidates among the features at least one datapoint in the one or more datapoints, wherein the perturbation candidates are one or more changes to features associated with the at least one datapoint, comprising:
• generating a modified datapoint based on changed feature values associated with the perturbation candidates and the at least one datapoint;
• formulate a CSP based on the first and second sets of CSP rules;
• solve the formulated CSP using a solver;
• use the solution of the CSP as a counterfactual explanation of a prediction made by the machine learning model with respect to the at least one datapoint, wherein the counterfactual explanation is a natural language representation of the solution of the CSP;
• and
• responsive to an accessible loss function of the machine learning model, iterate until an opposite classification is achieved, and subsequently using a set of constraints as the second set of CSP rules, wherein, in each iteration, the modified datapoint of a previous iteration serves as the at least one datapoint of a current iteration.

A computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor to:

• automatically analyze a machine learning dataset comprising at least one datapoints to deduce constraints on features of the at least one datapoint;
• generate a first set of CSP (Constraint Satisfaction Problem) rules expressing the constraints;
• based on a machine learning model trained on the machine learning dataset, generate a second set of CSP rules that define one or more perturbation candidates among the features at least one datapoint, wherein the perturbation candidates are one or more changes to features associated with the at least one datapoint, comprising:
• generating a modified datapoint based on changed feature values associated with the perturbation candidates and the at least one datapoint;
• formulate a CSP based on the first and second sets of CSP rules;
• solve the formulated CSP using a solver;
• use the solution of the CSP as a counterfactual explanation of a prediction made by the machine learning model with respect to the at least one datapoint, wherein the counterfactual explanation is a natural language representation of the solution of the CSP;
• and
• responsive to an accessible loss function of the machine learning model, iterate until an opposite classification is achieved, and subsequently using a set of constraints as the second set of CSP rules, wherein, in each iteration, the modified datapoint of a previous iteration serves as the at least one datapoint of a current iteration.