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.
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Status
- Active PAR
- PAR Approval
- 2022-11-10
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
AIFE-WG - AI Fairness Evaluation Working Group
- IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Qing An
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
No Active Projects
Standards approved by the IEEE SA Standards Board that are within the 10-year lifecycle.
No Active Standards
These standards have been replaced with a revised version of the standard, or by a compilation of the original active standard and all its existing amendments, corrigenda, and errata.
No Superseded Standards
These standards have been removed from active status through a ballot where the standard is made inactive as a consensus decision of a balloting group.
No Inactive-Withdrawn Standards
These standards are removed from active status through an administrative process for standards that have not undergone a revision process within 10 years.
No Inactive-Reserved Standards