Enhancing fairness in transfer learning for machine learning models with missing protected attributes in source or target domains

A computer program product for correcting source data used in machine learning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• receive, by the processor, first data;
• provide, by the processor, transfer learning processing in absence of one or more protected attributes using a covariate shift combined with re-weighing to reduce a difference in group-specific prevalences for the source data;
• wherein the source data is partially labeled data, the first data is target data that includes the one or more protected attributes, and the transfer learning processing uses a target-fair covariate shift;
• wherein the target-fair covariate shift uses weights that are chosen to minimize a linear combination of a fairness loss with a classification loss;
• and
• wherein the fairness loss is evaluated on the target data where the one or more protected attributes are available.

An apparatus comprising:

• a memory configured to store instructions;
• and
• a processor configured to execute the instructions to:
• receive first data;
• provide transfer learning processing in absence of one or more protected attributes using a covariate shift combined with re-weighing to reduce a difference in group-specific prevalences for a source data;
• wherein the source data is partially labeled data, the first data is target data that includes the one or more protected attributes, and the transfer learning processing uses a target-fair covariate shift;
• and
• wherein the target-fair covariate shift uses weights that are chosen to minimize a linear combination of a fairness loss with a classification loss, and the fairness loss is evaluated on the target data where the one or more protected attributes are available.