In various examples, sets of testing data may be selected and applied to an MLM such that differences in performance of the MLM in the testing between the sets indicates and may be used to determine whether and/or an extent by which the MLM is trained to rely on artifacts. Training data for the MLM may be generated using a first value of a parameter that defines a value of a characteristic of the training data. For testing, first testing data may be selected that corresponds to a second value of the parameter that shifts the value in a first direction and second testing data may be selected that corresponds to a third value of the parameter that shifts the value in a second direction (e.g., opposite the first direction). Various possible actions may be taken based on results of analyzing the differences in performance.