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Towards Region-aware Bias Evaluation Metrics

Angana Borah, Aparna Garimella, Rada Mihalcea

TL;DR

This paper addresses regional variation in gender bias as expressed by language models, arguing that fixed, universal bias dimensions fail to capture culturally specific stereotypes. It introduces a bottom-up, region-aware framework that first identifies region-specific F/M topics via topic modeling, then forms region-biased topic pairs through embedding similarities, and finally validates these dimensions with IAT-style human studies and region-aware WEAT evaluations. The authors demonstrate region-dependent bias dimensions across GeoWAC-derived topics and show that LLM outputs align with region-aware biases in highly represented regions but misalign for underrepresented regions, highlighting the need for region-sensitive bias metrics. The work advances bias evaluation in NLP by coupling data-driven topic discovery with human validation and cross-model analyses, informing more accurate region-aware assessment and potential mitigation strategies for multilingual and culturally diverse contexts.

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.

Towards Region-aware Bias Evaluation Metrics

TL;DR

This paper addresses regional variation in gender bias as expressed by language models, arguing that fixed, universal bias dimensions fail to capture culturally specific stereotypes. It introduces a bottom-up, region-aware framework that first identifies region-specific F/M topics via topic modeling, then forms region-biased topic pairs through embedding similarities, and finally validates these dimensions with IAT-style human studies and region-aware WEAT evaluations. The authors demonstrate region-dependent bias dimensions across GeoWAC-derived topics and show that LLM outputs align with region-aware biases in highly represented regions but misalign for underrepresented regions, highlighting the need for region-sensitive bias metrics. The work advances bias evaluation in NLP by coupling data-driven topic discovery with human validation and cross-model analyses, informing more accurate region-aware assessment and potential mitigation strategies for multilingual and culturally diverse contexts.

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.
Paper Structure (36 sections, 9 figures, 12 tables)

This paper contains 36 sections, 9 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Data
  3. Variations in Gender Bias Tests Across Regions
  4. A Method to Automatically Detect Bias Dimensions Across Regions
  5. Identifying Region-wise Bias Topics
  6. Finding Topic Pairs as Region-wise Bias Dimension Indicators
  7. Human Validation Setup
  8. Results: Bias Dimensions across Regions
  9. Region-wise Bias Topics
  10. Region-wise Bias Dimensions
  11. Unigram/Bigram Analysis
  12. Human Validation Results
  13. WEAT-based Evaluation Using Region-aware Topic Pairs
  14. Alignment of Region-Aware Bias Dimensions with LLM outputs
  15. Related Work
  16. ...and 21 more sections

Figures (9)

  • Figure 1: Methodology Pipeline: Stage 1 refers to the extraction of region-aware gender topics using topic modeling, Stage 2 refers to extraction of region-aware gender topic pairs using an embedding based approach
  • Figure 2: IAT-style test with region-aware topic pairs for human validation. The above example shows the user implicitly associates female to parenting and male to movies: When guidelines are reversed, they take longer time. Note that we randomize the order of tests for participants to ensure initial pairing bias is accounted for. Also, we have several pages showing faces and topics for each guideline.
  • Figure 3: Human validation results across regions. 'Unreversed' refers to bias dimensions with the same gender associations as our topic pairs, 'Reversed' refers to bias dimensions with the opposite gender associations.
  • Figure 4: Example Prompt for Persona Generation
  • Figure 5: Bias Evaluation of LLM outputs using region-aware bias topic pairs through 'persona generation'.
  • ...and 4 more figures