Monitoring machine learning models using surrogate model output

Abstract

In some aspects, a computing system may use a surrogate machine learning model to detect whether a production or other machine learning model has a tendency to generate different output depending on which subpopulation a particular sample belongs to. The surrogate machine learning model may be trained using features/outputs that are not included in the data used by the production model. For example, by using demographic information in lieu of the original labels of a dataset that was used to train a production model, a surrogate model may be used to detect whether the production model is able to discern one or more characteristics associated with but not present in a sample using other features of the dataset. Output of the surrogate machine learning model may be clustered to detect whether certain subpopulations are treated differently by the production model.