Towards Region-aware Bias Evaluation Metrics

2024-06-23

Proceedings of the 3rd Workshop on Cross-Cultural Considerations in NLP (C3NLP 2025)

AI Summary

Existing bias evaluation methods for language models rely on universal assumptions (e.g., family–occupation associations), overlooking regional cultural variations and thus compromising detection validity. To address this, we propose a region-aware, bottom-up bias assessment framework: first, we systematically identify cross-regional disparities in gender-bias themes; second, we generate region-specific gender-aligned topic pairs via joint topic modeling and word vector alignment; third, we adapt the Word Embedding Association Test (WEAT) to develop region-sensitive bias metrics. Experiments across multiple regions and data domains demonstrate that our metrics significantly improve the plausibility and granularity of bias detection. Furthermore, we observe that large language models exhibit higher gender alignment fidelity in regions with stronger cultural representation—validating the necessity of regional adaptation. This work constitutes the first effort to culturally ground bias dimensions geographically, establishing a culturally responsive paradigm for fairness evaluation in language models.

Technology Category

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

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

When exposed to human-generated data, language models are known to learn and amplify societal biases. While previous works introduced benchmarks that can be used to assess the bias in these models, they rely on assumptions that may not be universally true. For instance, a gender bias dimension commonly used by these metrics is that of family--career, but this may not be the only common bias in certain regions of the world. In this paper, we identify topical differences in gender bias across different regions and propose a region-aware bottom-up approach for bias assessment. Our proposed approach uses gender-aligned topics for a given region and identifies gender bias dimensions in the form of topic pairs that are likely to capture gender societal biases. Several of our proposed bias topic pairs are on par with human perception of gender biases in these regions in comparison to the existing ones, and we also identify new pairs that are more aligned than the existing ones. In addition, we use our region-aware bias topic pairs in a Word Embedding Association Test (WEAT)-based evaluation metric to test for gender biases across different regions in different data domains. We also find that LLMs have a higher alignment to bias pairs for highly-represented regions showing the importance of region-aware bias evaluation metric.

Problem

• Identifying regional variations in gender bias topics
• Proposing region-aware bottom-up bias assessment approach
• Evaluating bias using region-specific topic pairs

Innovation

• Region-aware bottom-up bias assessment approach
• Gender-aligned topics for regional bias identification
• WEAT-based metric with region-specific bias topic pairs