This document specifies a method for evaluating the fairness of machine learning. Multiple causes contribute to the unfairness of machine learning. In this document, these causes of machine learning unfairness are categorized. The widely recognized and used definitions of machine learning fairness are presented. This document also specifies various metrics corresponding to the definitions, and how to calculate the metrics. Test cases in this document give detailed conditions and procedures to set up the tests for evaluating machine learning fairness.

This standard specifies a method for evaluating the fairness of machine learning. Multiple causes contribute to the unfairness of machine learning. In the standard, these causes of machine leaning unfairness are categorized. The widely recognized and used definitions of machine learning are presented. The standard also specifies various metrics corresponding to the definitions, and how to calculate the metrics. Test cases in the standard give detailed conditions and procedures to set up tests for evaluating machine learning fairness.

The purpose of this standard is to help stakeholders of machine learning systems to gain a better understanding of the fairness aspects of an AI system. Using the standard, the stakeholders can test and obtain quantitative values for different fairness metrics, which can be used to measure whether a machine learning system achieves the intended fairness requirements.