Abstract: A method of providing a surrogate program for a program endpoint includes: obtaining, by a processor set, a set of plural input/output pairs generated using the program endpoint; generating, by the processor set, transformations based on the input/output pairs; generating, by the processor set, a model that classifies inputs of the input/output pairs to ones of the transformations based on parameters of one or more strings of the inputs; receiving, by the processor set, a new input; selecting, by the processor set and using the model, one of the transformations based on parameters of one or more strings of the new input; and generating, by the processor set, a new output by applying the selected one of the transformations to the new input.

Abstract: One embodiment provides a method, including: providing, from the central server to a machine-learning model, a training set of samples having known values for at least one target protected attribute, wherein the training set includes a first set of samples having a first value for the at least one target protected attribute and a second set of samples having a second value for the at least one target protected attribute; receiving, at the central server from the machine-learning model, an output classification for each of the samples within the training set of samples; and generating, at the central server using the output classification, a set of rules delineating a region within the machine-learning model as discriminatory, wherein the region includes a classification region where the machine-learning model classifies received samples differently based upon a value of the at least one protected attribute.