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

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xuanqi Gao, Chao Shen, Weipeng Jiang, Chenhao Lin, Qian Li, Qian Wang, Qi Li, Xiaohong Guan
{"title":"Fairness in machine learning: definition, testing, debugging, and application","authors":"Xuanqi Gao, Chao Shen, Weipeng Jiang, Chenhao Lin, Qian Li, Qian Wang, Qi Li, Xiaohong Guan","doi":"10.1007/s11432-023-4060-x","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":"1 1","pages":""},"PeriodicalIF":7.3000,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science China Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11432-023-4060-x","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

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.

机器学习的公平性:定义、测试、调试和应用
近年来,人工智能技术被广泛应用于计算机视觉、自然语言处理和自动驾驶等诸多领域。机器学习算法作为人工智能的核心技术,极大地方便了人们的生活。然而,机器学习系统中潜藏的公平性问题会给个体公平和社会安全带来风险。研究公平性的定义、问题的来源以及公平性的测试和调试方法,有助于确保机器学习系统的公平性,促进人工智能技术在各个领域的广泛应用。本文介绍了机器学习公平性的相关定义,分析了公平性问题的来源。此外,本文还对公平性测试和调试方法进行了指导,并总结了流行的数据集。本文还讨论了机器学习公平性的技术进展,并强调了该领域未来的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Science China Information Sciences
Science China Information Sciences COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
12.60
自引率
5.70%
发文量
224
审稿时长
8.3 months
期刊介绍: Science China Information Sciences is a dedicated journal that showcases high-quality, original research across various domains of information sciences. It encompasses Computer Science & Technologies, Control Science & Engineering, Information & Communication Engineering, Microelectronics & Solid-State Electronics, and Quantum Information, providing a platform for the dissemination of significant contributions in these fields.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信