Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey

被引：0

作者：

Bartl, Marion ^{[1
,2
]}

Mandal, Abhishek ^{[2
,3
]}

Leavy, Susan ^{[1
,2
]}

Little, Suzanne ^{[2
,3
]}

机构：

[1] Univ Coll Dublin, Sch Informat & Commun Studies, Dublin, Ireland

[2] Insight Res Ireland Ctr Data Analyt, Dublin, Ireland

[3] Dublin City Univ, Sch Comp, Dublin, Ireland

来源：

ACM COMPUTING SURVEYS | 2025年 / 57卷 / 06期

基金：

爱尔兰科学基金会;

关键词：

Trustworthy AI; ethical AI; natural language processing; computer vision;

D O I：

10.1145/3700438

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Taking an interdisciplinary approach to surveying issues around gender bias in textual and visual AI, we present literature on gender bias detection and mitigation in NLP, CV, as well as combined visual-linguistic models. We identify conceptual parallels between these strands of research as well as how methodologies were adapted cross-disciplinary from NLP to CV. We also find that there is a growing awareness for theoretical frameworks from the social sciences around gender in NLP that could be beneficial for aligning bias analytics in CV with human values and conceptualising gender beyond the binary categories of male/female.

引用

页数：36

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