Systems and methods for providing a systemic error in artificial intelligence algorithms

A method comprising:

• generating, based on a training dataset, a reference model and a surrogate model;
• selecting datapoints from the training dataset that are predicted correctly by a source model as a group of adversarial candidates;
• selecting, from the group of adversarial candidates, a sub-group of candidates that each have a low confidence score according to a threshold to yield a sub-group of adversarial candidates;
• adding noise to each candidate of the sub-group of adversarial candidates to yield a noisy group of adversarial examples;
• testing the noisy group of adversarial examples against the source model to obtain a set of source model outputs that the source model predicts correctly;
• testing the set of source model successful adversarial examples against the reference model to yield a set of reference model outputs;
• testing the set of source model successful adversarial examples against the surrogate model to yield a set of surrogate model outputs;
• and
• identifying a set of fingerprints based on which ones from the set of source model outputs, the set of reference model outputs and the set of surrogate model outputs pass as adversarial examples against the source model and the surrogate model, but not the reference model.