CPC G06N 20/20 (2019.01) [G06F 18/2148 (2023.01); G06N 3/045 (2023.01); G06N 7/01 (2023.01); G06Q 40/03 (2023.01)] | 22 Claims |
1. A method implemented by a modelling system and comprising:
executing a machine learning model building library to iteratively train each of:
an adversarial classifier that is constructed to predict a value for sensitive attributes based on an output generated by a predictive model, wherein iteratively training the adversarial classifier comprises adjusting first parameters of the adversarial classifier to decrease a first value of a first prediction loss metric for the adversarial classifier, and
the predictive model, wherein iteratively training the predictive model comprises adjusting second parameters of the predictive model to decrease a second value of an objective function for the predictive model, wherein the objective function is a difference between a second prediction loss metric for the predictive model and the first prediction loss metric;
determining that a fairness metric threshold is satisfied by the iteratively trained predictive model, wherein predictions of the iteratively trained predictive model provide for improved fairness outcomes as a result of the adversarial classifier correctly classifying one or more of the sensitive attributes; and
providing the iteratively trained predictive model to a model execution system for deployment in a production environment, wherein the iteratively trained predictive model comprises a credit model configured to generate credit decisions with respect to credit applicants.
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