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.

中文翻译：

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自然语言处理和计算机视觉中的性别偏见：比较调查


我们采用跨学科方法来调查文本和视觉 AI 中的性别偏见问题，展示了有关 NLP、CV 中性别偏见检测和缓解的文献，以及视觉语言学模型的组合。我们确定了这些研究链之间的概念相似之处，以及方法如何从 NLP 到 CV 的跨学科调整。我们还发现，人们越来越意识到社会科学中围绕 NLP 中性别的理论框架，这可能有利于使简历中的偏见分析与人类价值观保持一致，并在男性/女性的二元类别之外概念化性别。