Exploring Gender Bias Beyond Occupational Titles

2025-07-03

AI Summary

This study uncovers implicit gender bias in language that extends beyond occupational stereotypes, focusing on non-occupational linguistic elements—particularly action verbs and object nouns. Method: We propose the first fine-grained, multilingual (including Japanese) gender bias evaluation framework, comprising the GenderLexicon dataset and an interpretable, context-aware bias quantification model. Unlike conventional occupation-based bias detection, our approach systematically identifies and attributes gender bias to dynamic semantic units such as verb–noun collocations. Contribution/Results: Experiments across five cross-lingual, multi-domain datasets demonstrate that such bias is pervasive in everyday syntactic and semantic structures. Our model achieves strong generalizability and interpretability, enabling precise bias localization and causal attribution. This work establishes a novel paradigm for bias溯源 (traceability) and mitigation, advancing both computational linguistics and fairness-aware NLP.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Investigating gender bias in action verbs and object nouns
• Developing a framework to quantify contextual gender bias
• Validating bias detection across diverse datasets including Japanese

Innovation

• Novel dataset GenderLexicon for bias analysis
• Framework estimating contextual and gender bias
• Model scores bias for improved explainability