Abstract: Methods, non-transitory computer readable media, and model evaluations systems for understanding diverse machine learning models (MLMs) are disclosed. In some examples, a feature contribution value is determined for features included in a reference or evaluation input data set. The evaluation input data set represents a protected class population and each feature contribution value identifies a contribution by a feature to a difference in output generated by an MLM for the evaluation input data set. Model explanation information is generated using the feature contribution values and execution of the MLM is monitored. The model explanation information explains the difference in output generated by the MLM for the evaluation input data set and includes information relating to a model-based decision. A report is generated from a knowledge graph for the MLM and output via a GUI to an operator device that includes the model explanation information.

Abstract: A method, in some implementations, may include obtaining output from a machine learning (ML) model responsive to input data, obtaining initial training data representing training data used to train the ML model, generating, based on the output from the ML model and the initial training data, correction training data that represents a desired alteration to the output from the ML model responsive to one or more particular subgroups in the input data, generating, based on the correction training data, a correction ML model configured to receive, as input, the input data and to output correction values which, when combined with the output from the ML model, perform the desired alteration, and generating corrected output as a combination of the output from the ML model and the output correction values from the correction ML model, and providing, for display, the corrected output.

Abstract: Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.

Abstract: Systems and methods for understanding diverse machine learning models.

Abstract: Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.

Abstract: Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.

Abstract: Systems and methods for understanding diverse machine learning models.