Computing system and method for creating a machine learning model having improved fairness

A non-transitory computer-readable medium, wherein the non-transitory computer-readable medium is provisioned with program instructions that, when executed by at least one processor, cause a computing platform to:

• utilize a first machine learning process to train a machine learning model that is configured to (i) receive an input record comprising feature values for an input set of features and (ii) output an unadjusted predicted value for at least one numerical variable;
• initialize a subpopulation-dependent fairer version of the trained machine learning model that is configured to (i) receive an input record comprising feature values for the input set of features and a subpopulation value indicating that the input record is associated with a particular subpopulation from a group of distinct subpopulations and (ii) output a fairness-adjusted predicted value for the at least one numerical variable that is dependent on the subpopulation value;
• utilize a second machine learning process to train a subpopulation-independent projection of the subpopulation-dependent fairer version of the trained machine learning model that is configured to (i) receive an input record comprising feature values for the input set of features and (ii) output a fairness-adjusted predicted value for the at least one numerical variable without relying on any subpopulation value;
• evaluate multiple candidate instances of a subpopulation-independent fairer version of the trained machine learning model comprising a linear combination of (i) an unadjusted predicted value for the at least one numerical variable that is output by the trained machine learning model and (ii) a fairness-adjusted predicted value for the at least one numerical variable that is output by the subpopulation-independent projection, wherein each respective candidate instance of the subpopulation-independent fairer version of the trained machine learning model applies a different weighting to the unadjusted and fairness-adjusted predicted values within the linear combination;
• and
• based on evaluating the multiple candidate instances of the subpopulation-independent fairer version of the trained machine learning model, select a given candidate instance of the subpopulation-independent fairer version of the trained machine learning model, wherein the subpopulation-independent fairer version of the trained machine learning model is thereafter deployed.

A method carried out by a computing platform, the method comprising:

• utilizing a first machine learning process to train a machine learning model that is configured to (i) receive an input record comprising feature values for an input set of features and (ii) output an unadjusted predicted value for at least one numerical variable;
• initializing a subpopulation-dependent fairer version of the trained machine learning model that is configured to (i) receive an input record comprising feature values for the input set of features and a subpopulation value indicating that the input record is associated with a particular subpopulation from a group of distinct subpopulations and (ii) output a fairness-adjusted predicted value for the at least one numerical variable that is dependent on the subpopulation value;
• utilizing a second machine learning process to train a subpopulation-independent projection of the subpopulation-dependent fairer version of the trained machine learning model that is configured to (i) receive an input record comprising feature values for the input set of features and (ii) output a fairness-adjusted predicted value for the at least one numerical variable without relying on any subpopulation value;
• evaluating multiple candidate instances of a subpopulation-independent fairer version of the trained machine learning model comprising a linear combination of (i) an unadjusted predicted value for the at least one numerical variable that is output by the trained machine learning model and (ii) a fairness-adjusted predicted value for the at least one numerical variable that is output by the subpopulation-independent projection, wherein each respective candidate instance of the subpopulation-independent fairer version of the trained machine learning model applies a different weighting to the unadjusted and fairness-adjusted predicted values within the linear combination;
• and
• based on evaluating the multiple candidate instances of the subpopulation-independent fairer version of the trained machine learning model, selecting a given candidate instance of the subpopulation-independent fairer version of the trained machine learning model, wherein the subpopulation-independent fairer version of the trained machine learning model is thereafter deployed.