Addressing Overcommitment in the Reasoning of Gendered Economic Memes under Multimodal Ambiguity
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.
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Key points
- 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 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 evaluate CGER-Net on EconMeme-GE, a curated dataset of image-text memes annotated as Men, Women, Neutral, or Ambiguous.
Sources (1)
- [1]Addressing Overcommitment in the Reasoning of Gendered Economic Memes under Multimodal AmbiguityarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 11:24 AM
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.
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.
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