Machine learning fairness notions: Bridging the gap with real-world applications
作者：
Highlights：
• A catalog of machine learning fairness notions.
• Machine learning fairness notions explained with a large number of figures, tables, and examples.
• A novel list of criteria for the applicability of machine learning fairness notions.
• A decision diagram to be used by practitioners and policy makers to navigate the large catalog of fairness notions.
摘要
•A catalog of machine learning fairness notions.•Machine learning fairness notions explained with a large number of figures, tables, and examples.•A novel list of criteria for the applicability of machine learning fairness notions.•A decision diagram to be used by practitioners and policy makers to navigate the large catalog of fairness notions.
论文关键词：Fairness,Machine learning,Discrimination,Survey,Systemization of Knowledge (SoK)
论文评审过程：Received 30 November 2020, Revised 9 April 2021, Accepted 12 May 2021, Available online 1 June 2021, Version of Record 1 June 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102642
