Zestfinance, Inc.

Mark Frederick Eberstein

Jerome Louis Budzik

Sean Javad Kamkar

John Wickens Lamb Merrill

Jose Efrain Valentin

Feng Li

Michael Egan Van Veen

John Joseph Beahan, Jr

Geoffrey Michael Ward

John Wickens Merrill

Systems and methods for training models to improve fairness.

Systems and methods for model fairness

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.

Systems and methods for decomposition of differentiable and non-differentiable models

Systems and methods for understanding diverse machine learning models.

Growing community of inventors

Los Angeles, CA, United States of America

Mark Frederick Eberstein