A computer-implemented method for assessing a potential divergence of an outcome predicted by a machine learning system including training a model on a first set of observations, each observation being associated with a target value, randomly generating a second set of observations, applying the trained model to the second set thereby obtaining a target value associated with each observation of the second set, indexing the first and second sets of observations and their associated target values into an index, receiving a first query allowing a selection of a subset of the first and second sets of observations, generating a second query, generating a third query that comprises the first query and an additional constraint, querying the index using the second and third queries, and returning a response to the second and third queries.