Addressing Overcommitment in the Reasoning of Gendered Economic Memes under Multimodal Ambiguity

2026-10-08

AI Summary

This study addresses the tendency of multimodal models to over-rely on pretraining biases when interpreting gendered economic memes due to insufficient contextual evidence, resulting in stereotypical attributions. We propose CGER-Net, a novel framework introducing an evidence-gated reasoning mechanism based on contextual sufficiency. By identifying cognitive overcommitment as the primary source of bias, this approach enables models to infer confidently when evidence is explicit and abstain principledly when ambiguous. To validate the framework, we construct the EconMeme-GE dataset. Experimental results demonstrate that CGER-Net reduces the gender overcommitment rate by 44% on ambiguous samples while maintaining accuracy on unambiguous cases. Furthermore, 79% of the generated rationales align with evidential logic, underscoring the critical value of determining when not to infer.

Abstract

Multimodal meme understanding is increasingly used to analyze socially sensitive content, yet existing models often exhibit biased behavior when interpreting economic dependence and social roles under ambiguity. Many memes express economic relationships through sparse text or symbolic visual cues, providing insufficient evidence for gendered attribution. In such underspecified settings, models tend to rely on pretraining correlations, leading to hallucinated and stereotypical economic role assignments. In this work, we study gendered economic dependence in image-text memes through the lens of contextual sufficiency and identify epistemic overcommitment-inferring roles without adequate evidence-as a primary source of bias. We propose CGER-Net, a context-grounded multimodal framework that estimates whether the input provides sufficient evidence for gendered economic reasoning and applies evidence-gated inference to enable confident attribution when cues are explicit while favoring principled abstention otherwise. We evaluate CGER-Net on EconMeme-GE, a curated dataset of image-text memes annotated as Men, Women, Neutral, or Ambiguous. Across strong contemporary multimodal baselines, CGER-Net reduces Gender Overcommitment Rate by up to 44% on ambiguous instances while maintaining comparable accuracy on unambiguous cases. Human evaluation further shows that 79% of generated rationales are judged as epistemically aligned with the available evidence. These results highlight the importance of modeling when not to infer for reliable and responsible multimodal analysis.

Problem

• multimodal meme understanding
• gender bias
• epistemic overcommitment
• economic dependence
• ambiguity

Innovation

• Multimodal meme understanding
• Epistemic overcommitment
• Evidence-gated inference
• Context-grounded reasoning
• Bias mitigation