Training machine learning models using privacy preserving fairness regularization
AssigneeAmazon Technologies, Inc.
InventorsRan Wolff, Morgan Dumitru
Life of this patent
Estimating the term…
Abstract
Devices and techniques are generally described for privacy preserving fairness regularization for machine learning. In some examples, a first machine learning model may be trained for a first predictive task using unrestricted data. First data classified as restricted data may be determined. A second machine learning model may be trained to predict model fairness based at least in part on the first data and further based at least in part on second data representing characteristics of the first machine learning model during training. The second machine learning model may generate third data representing model fairness of a third machine learning model.
Claims20
A computer-implemented method comprising:
- generating, by at least one processor, a first trained machine learning model by training a first machine learning model using a first dataset, wherein the first dataset comprises unrestricted data;
- determining first data classified as restricted data, wherein the restricted data identifies different classes of data points of the first dataset;
- determining second data comprising error statistics determined from the training of the first machine learning model;
- determining third data describing weights of the first trained machine learning model;
- determining fourth data representing a fairness score for the first trained machine learning model, wherein the fourth data is determined based on respective model performance for the different classes of the data points of the first dataset;
- generating a first training instance comprising the second data, the third data, and the fourth data;
- and
- generating a second trained machine learning model using the first training instance, wherein the second trained machine learning model is trained to predict model fairness scores from input data representing input error statistics and input model weights.
A method comprising:
- training, by at least one processor, a first machine learning model for a first predictive task, wherein the first machine learning model is trained using a first dataset comprising unrestricted data;
- determining first data associated with the first dataset, wherein access to the first data is restricted;
- training, by the at least one processor, a second machine learning model to predict model fairness, wherein the second machine learning model is trained based at least in part on the first data and on second data representing characteristics of the first machine learning model determined after training;
- and
- generating, by the second machine learning model, third data representing the model fairness of a third machine learning model.
A system comprising:
- at least one processor;
- and
- non-transitory computer-readable memory storing instructions that, when executed by the at least one processor, are effective to cause the at least one processor to:
- train a first machine learning model for a first predictive task, wherein the first machine learning model is trained using a first dataset comprising unrestricted data;
- determine first data associated with the first dataset, wherein access to the first data is restricted;
- train a second machine learning model to predict model fairness, wherein the second machine learning model is trained based at least in part on the first data and further based at least in part on second data representing characteristics of the first machine learning model determined after training;
- and
- generate, by the second machine learning model, third data representing model fairness of a third machine learning model.
Dependencies are inferred from the claim text.