Abstract: Techniques for generating counterfactuals in connection with machine learning models. The techniques include applying a trained machine learning model to an input to obtain a first outcome; determining whether the first outcome has a value in a set of one or more target values; when it is determined that the first outcome does not have a value in the set of one or more target values, generating a counterfactual input at least in part by applying a trained neural network model to the input to obtain a corresponding output, the corresponding output indicating changes to be made to one or more values of one or more attributes of the input to obtain the counterfactual input, and generating feedback based on the counterfactual input.

Abstract: Techniques for generating counterfactuals in connection with machine learning models. The techniques include applying a trained machine learning model to an input to obtain a first outcome; determining whether the first outcome has a value in a set of one or more target values; when it is determined that the first outcome does not have a value in the set of one or more target values, generating a counterfactual input at least in part by applying a trained neural network model to the input to obtain a corresponding output, the corresponding output indicating changes to be made to one or more values of one or more attributes of the input to obtain the counterfactual input, and generating feedback based on the counterfactual input.