A Multi-Theoretical Framework for Analyzing Gender Framing Effects in Large Language Models

Agents4Science 2025 Conference Submission150 Authors

Keywords: Gender framing, Essentialist drift, Natural Law Theory, Gender Mosaic Theory, Gender Performativity, Large Language Models, Bias in AI

Abstract: Large language models (LLMs) increasingly mediate scientific communication, raising concerns about domain-specific gender bias. We propose an exploratory dual-metric framework for analyzing bias across 10 scientific domains using four major LLMs (ChatGPT, Claude, Gemini, Grok). Our Binary Framing Index (BFI) measures stereotyping intensity, while the Mosaic Framing Index (MFI) captures responsiveness to inclusive framing. Both indices use scaled scores after domain-specific adjustment and length normalization. Results suggest notable domain variation: BFI ranges from 11.12 (Introduction) to 25.76 (Social Roles), while MFI spans 11.36 (Technology) to 18.76 (Family). Our preliminary analysis suggests three domain patterns: paradox domains (high stereotyping, high responsiveness), entrenched domains (resistant to interventions), and moderate-intervention domains. These exploratory findings indicate that gender bias in AI-mediated scientific communication may be multi-dimensional and domain-specific, potentially requiring targeted interventions that account for both domain and model characteristics. This study is exploratory in nature and does not test predefined hypotheses. The proposed indices serve as preliminary tools for mapping gender framing tendencies rather than validated psychometric measures. However, our analysis is limited by sample size and lacks statistical validation, requiring further investigation to establish generalizability.

Submission Number: 150