自然语言处理和计算机视觉中的性别偏见：比较调查

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2024-11-02 DOI:10.1145/3700438

Marion Bartl, Abhishek Mandal, Susan Leavy, Suzanne Little

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引用次数: 0

摘要

我们采用跨学科的方法来研究文本和视觉人工智能中的性别偏见问题，介绍了在 NLP、CV 以及视觉-语言组合模型中检测和减轻性别偏见的文献。我们发现了这些研究领域在概念上的相似之处，以及从 NLP 到 CV 的跨学科方法是如何调整的。我们还发现，越来越多的人意识到社会科学的理论框架与 NLP 中的性别问题有关，这些理论框架有助于将 CV 中的偏差分析与人类价值观相统一，并将性别概念化，使其超越男性/女性的二元分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.