Fairness in machine learning:definition,testing,debugging,and application  

作　　者：Xuanqi GAO Chao SHEN Weipeng JIANG Chenhao LIN Qian LI Qian WANG Qi LI Xiaohong GUAN 

机构地区：[1]Faculty of Electronic and Information Engineering,Xi’an Jiaotong University,Xi’an 710049,China [2]School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China [3]Institute for Network Sciences and Cyberspace,Tsinghua University,Beijing 100084,China

出　　处：《Science China(Information Sciences)》2024年第9期37-57,共21页中国科学（信息科学）（英文版）

基　　金：partially supported by National Key R&D Program of China(Grant No.2020AAA0107702);National Natural Science Foundation of China(Grant Nos.U21B2018,62161160337,62132011,62206217,62376210,62006181,U20B2049,U20A20177);Shaanxi Province Key Industry Innovation Program(Grant Nos.2023-ZDLGY-38,2021ZDLGY01-02);China Postdoctoral Science Foundation(Grant Nos.2022M722530,2023T160512);Fundamental Research Funds for the Central Universities(Grant Nos.xtr052023004,xtr022019002,xzy012022082)。

摘　　要：In recent years,artificial intelligence technology has been widely used in many fields,such as computer vision,natural language processing and autonomous driving.Machine learning algorithms,as the core technique of AI,have significantly facilitated people's lives.However,underlying fairness issues in machine learning systems can pose risks to individual fairness and social security.Studying fairness definitions,sources of problems,and testing and debugging methods of fairness can help ensure the fairness of machine learning systems and promote the wide application of artificial intelligence technology in various fields.This paper introduces relevant definitions of machine learning fairness and analyzes the sources of fairness problems.Besides,it provides guidance on fairness testing and debugging methods and summarizes popular datasets.This paper also discusses the technical advancements in machine learning fairness and highlights future challenges in this area.

关 键 词：artificial intelligence security machine learning security machine learning fairness model testing model debugging 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]