面向图像识别的公平性研究进展  

Review on fairness in image recognition

作　　者：王玫 邓伟洪[2] 苏森[2] Wang Mei;Deng Weihong;Su Sen(School of Artificial Intelligence,Beijing Normal University,Beijing 100875,China;School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China)

机构地区：[1]北京师范大学人工智能学院,北京100875 [2]北京邮电大学人工智能学院,北京100876

出　　处：《中国图象图形学报》2024年第7期1814-1833,共20页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62306043,62236003);中国博士后科学基金资助项目(2022M720517)。

摘　　要：在过去的几十年里,图像识别技术经历了迅速发展,并深刻地改变着人类社会的进程。发展图像识别技术的目的是通过减少人力劳动和增加便利来造福人类。然而,最近的研究和应用表明,图像识别系统可能会表现出偏见甚至歧视行为,从而对个人和社会产生潜在的负面影响。因此,图像识别的公平性研究受到广泛关注,避免图像识别系统可能给人们带来的偏见与歧视,才能使人完全信任该项技术并与之和谐相处。本文对图像识别的公平性研究进行了全面的梳理回顾。首先,简要介绍了偏见3个方面的来源,即数据不平衡、属性间的虚假关联和群体差异性;其次,对于常用的数据集和评价指标进行汇总;然后,将现有的去偏见算法划分为重加权(重采样)、图像增强、特征增强、特征解耦、度量学习、模型自适应和后处理7类,并分别对各类方法进行介绍,阐述了各方法的优缺点;最后,对该领域的未来研究方向和机遇挑战进行了总结和展望。整体而言,学术界对图像识别公平性的研究已经取得了较大的进展,然而该领域仍处于发展初期,数据集和评价指标仍有待完善,针对未知偏见的公平性算法有待研究,准确率和公平性的权衡困境有待突破,针对细分任务的独特发展趋势开始呈现,视频数据的去偏见算法逐渐受到关注。In the past few decades,image recognition technology has undergone rapid developments and has been integrated into people's lives,profoundly changing the course of human society.However,recent studies and applications indicate that image recognition systems would show human-like discriminatory bias or make unfair decisions toward certain groups or populations,even reducing the quality of their performances in historically underserved populations.Consequently,the need to guarantee fairness for image recognition systems and prevent discriminatory decisions to allow people to fully trust and live in harmony has been increasing.This paper presents a comprehensive overview of the cutting-edge research progress toward fairness in image recognition.First,fairness is defined as achieving consistent performances across different groups regardless of peripheral attributes(e.g.,color,background,gender,and race) and the reasons for the emergence of bias are illustrated from three aspects.1) Data imbalance.In existing datasets,some groups are overrepresented and others are underrepresented.Deep models will facilitate optimization for the overrepresented groups to boost the accuracy,while the underrepresented ones are ignored during training.2) Spurious correlations.Existing methods continuously capture unintended decision rules from spurious correlations between target variables and peripheral attributes,failing to generalize the images with no such correlations.3) Group discrepancy.A large discrepancy exists between different groups.Performance on some subjects is sacrificed when deep models cannot trade off the specific requirements of various groups.Second,datasets(e.g.,Colored Mixed National Institute of Standards and Technology database(MNIST),Corrupted Canadian Institute for Advanced Research-10 database(CIFAR-10),CelebFaces attributes database(CelebA),biased action recognition(BAR),and racial faces in the wild(RFW)) and evaluation metrics(e.g.,equal opportunity and equal odds) used for fairness in image recognition are

关 键 词：公平性 偏见 去偏见学习 图像识别 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]