挖掘职业外的性别偏见，提升模型解释力。
Exploring Gender Bias Beyond Occupational Titles
- 构建新数据集GenderLexicon，量化语境中的性别偏见
- 发现性别偏见不仅存在于职业标签，还蔓延至动词与名词
- 支持多语言验证，适用于跨文化偏见研究
本文研究性别与语境偏见之间的关联，关注动作动词、物体名词及职业等要素。提出新数据集GenderLexicon和分析框架，可量化并解释语境偏见及其性别相关性。模型输出偏见评分，增强可解释性。实验在五个多样化数据集上验证，涵盖日文数据集，结果证实性别偏见远超职业刻板印象，具有广泛存在性。
原文摘要 · Abstract (English)
In this work, we investigate the correlation between gender and contextual biases, focusing on elements such as action verbs, object nouns, and particularly on occupations. We introduce a novel dataset, GenderLexicon, and a framework that can estimate contextual bias and its related gender bias. Our model can interpret the bias with a score and thus improve the explainability of gender bias. Also, our findings confirm the existence of gender biases beyond occupational stereotypes. To validate our approach and demonstrate its effectiveness, we conduct evaluations on five diverse datasets, including a Japanese dataset.
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