System and method for auditing and enforcing conditional fairness via optimal transport

A system for auditing and enforcing conditional fairness in a machine learning model, the system comprising:

• a processor;
• and
• a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to:
• identify at least one feature associated with data that is inputted into the machine learning model;
• determine a first joint distribution of outputs from the machine learning model and a second feature from among the at least one feature based on a first level of a first feature from among the at least one feature and a second joint distribution of outputs from the machine learning model and the second feature based on a second level of the first feature;
• calculate corresponding conditional demographic disparity between the first joint distribution and the second joint distribution with respect to both the first feature and the second feature and with respect to both the first level and the second level;
• aggregate each conditional demographic disparity into a single conditional demographic disparity value;
• audit the conditional fairness by computing a distance between the first joint distribution and the second joint distribution based on the single conditional demographic disparity value;
• compute, based on the distance, a regularizer that reduces the conditional demographic disparity;
• and
• enforce the conditional fairness by applying the regularizer to the machine learning model, thereby substantially improving performance of the machine learning model in outputting results.

A non-transitory computer readable medium configured to store instructions for auditing and enforcing conditional fairness in a machine learning model, the instructions, when executed, cause a processor to perform the following:

• identifying at least one feature associated with data that is inputted into the machine learning model;
• determining a first joint distribution of outputs from the machine learning model and a second feature from among the at least one feature based on a first level of a first feature from among the at least one feature and a second joint distribution of outputs from the machine learning model and the second feature based on a second level of the first feature;
• calculating corresponding conditional demographic disparity between the first joint distribution and the second joint distribution with respect to both the first feature and the second feature and with respect to both the first level and the second level;
• aggregating each conditional demographic disparity into a single conditional demographic disparity value;
• auditing the conditional fairness by computing a distance between the first joint distribution and the second joint distribution based on the single conditional demographic disparity value;
• computing, based on the distance, a regularizer that reduces the conditional demographic disparity;
• and
• enforcing the conditional fairness by applying the regularizer to the machine learning model, thereby substantially improving performance of the machine learning model in outputting results.