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

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外文摘要：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.

外文关键词：

artificial intelligence securitymachine learning securitymachine learning fairnessmodel test-ingmodel debugging

作者：

Xuanqi GAO、Chao SHEN、Weipeng JIANG、Chenhao LIN、Qian LI、Qian WANG、Qi LI、Xiaohong GUAN

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作者单位：

Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China

School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China

Institute for Network Sciences and Cyberspace,Tsinghua University,Beijing 100084,China

基金：

National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaShaanxi Province Key Industry Innovation ProgramShaanxi Province Key Industry Innovation ProgramChina Postdoctoral Science FoundationChina Postdoctoral Science FoundationFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities

项目编号：

2020AAA0107702U21B20186216116033762132011622062176237621062006181U20B2049U20A201772023-ZDLGY-382021ZDLGY01-022022M7225302023T160512xtr052023004xtr022019002xzy012022082

出版年：

2024

DOI：

10.1007/s11432-023-4060-x

中国科学:信息科学(英文版)

中国科学院

中国科学:信息科学(英文版)

CSTPCDEI

影响因子：0.715

ISSN：1674-733X

年,卷(期)：2024.67(9)