Systems and methods for fairness-enhanced utility-preserving data transformation for attribute suppression

A non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:

• receiving an original dataset comprising a plurality of samples of tabular data, each sample having a plurality of attributes;
• receiving an identification of a utility attribute, a sensitive attribute, and a fairness attribute from the plurality of attributes;
• computing entropy statistics for the original dataset using the utility attribute, the sensitive attribute, and the fairness attribute;
• training auxiliary classifier models using the utility attribute, the sensitive attribute, and/or the fairness attribute;
• training an auxiliary feature extractor model using the attributes other than the utility attribute;
• training a transformation model with the auxiliary classifier models and the auxiliary feature extractor model;
• transforming the original dataset into a transformed dataset using the transformation model;
• and
• training a machine learning model using the transformed dataset, wherein the trained machine learning model is accurate in predicting the utility attribute, fair with respect to the fairness attribute, and does not leak information about the sensitive attribute.

A system, comprising:

• an original dataset comprising original dataset comprising a plurality of samples of tabular data, each sample having a plurality of attributes;
• a user electronic device executing a user computer program;
• and
• an electronic device executing a computer program that is configured to receive the original dataset;
• to receive an identification of a utility attribute, a sensitive attribute, and a fairness attribute from the plurality of attributes from the user computer program;
• to compute entropy statistics for the original dataset using the utility attribute, the sensitive attribute, and the fairness attribute;
• to train auxiliary classifier models using the utility attribute, the sensitive attribute, and/or the fairness attribute, wherein the auxiliary classifier models provide an estimate of whether transformed data produced by the transformation model complies with the utility attributes, sensitive attributes, and fairness attributes;
• to train an auxiliary feature extractor model using the attributes other than the utility attribute, wherein the auxiliary feature extractor model is trained to output a multidimensional representation of the sensitive attributes and the fairness attributes;
• to train a transformation model with the auxiliary classifier models and the auxiliary feature extractor model;
• to transform the original dataset into a transformed dataset using the transformation model;
• and to train a machine learning model using the transformed dataset, wherein the trained machine learning model maintains information associated with the utility attribute, maintains fairness with respect to the fairness attribute, and does not leak information about the sensitive attribute.