Method and device for validating explainability methods for a machine learning system

A device configured to validating an attribution-based explainability method for a machine learning system, the system configured to:

• ascertain synthetic data points using a generator according to specified noise vectors;
• ascertain outputs of the machine learning system by propagating the synthetic data points through the machine learning system and ascertaining explanation outputs using the attribution-based explainability method for the ascertained outputs;
• ascertain scores of the explanation outputs that characterize in which section the explanation outputs lie in a distribution of explainability scores;
• optimize one of the noise vectors with regard to the score in such a way that the score moves to a rear part of the distribution of explanability scores;
• ascertain a further synthetic data point using the generator according to the optimized noise vector;
• ascertain a further output of the machine learning system by propagating the further synthetic data point through the machine learning system and ascertaining a further explanation output using the attribution-based explainability method for the further ascertained output;
• ascertain a score of the further explanation output;
• and
• validate the attribution-based explainability method, wherein when the score lies in a rear part of a distribution of an empirically ascertained distribution of explainability scores and the further synthetic data point does not lie in a rear part of a distribution of the synthetic data points, a positive validation is given.

A non-transitory machine-readable storage medium on which is stored a computer program for validating an attribution-based explainability method for a machine learning system, the computer program, when executed by a computer, causing the computer to perform the following steps:

• ascertaining synthetic data points using a generator according to specified noise vectors;
• ascertaining outputs of the machine learning system by propagating the synthetic data points through the machine learning system and ascertaining explanation outputs using the attribution-based explainability method for the ascertained outputs;
• ascertaining scores of the explanation outputs that characterize in which section the explanation outputs lie in a distribution of explainability scores;
• optimizing one of the noise vectors with regard to the score in such a way that the score moves to a rear part of the distribution of explanability scores;
• ascertaining a further synthetic data point using the generator according to the optimized noise vector;
• ascertaining a further output of the machine learning system by propagating the further synthetic data point through the machine learning system and ascertaining a further explanation output using the attribution-based explainability method for the further ascertained output;
• ascertaining a score of the further explanation output;
• and
• validating the attribution-based explainability method, wherein when the score lies in a rear part of a distribution of an empirically ascertained distribution of explainability scores and the further synthetic data point does not lie in a rear part of a distribution of the synthetic data points, a positive validation is given.