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arXiv Machine Learning By Kushal Kanwar, Dushyant Singh Chauhan, Kapil Rana, Gopendra Vikram Singh, Nils Lukas

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

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The paper introduces CGER-Net, a context‑grounded multimodal framework designed to reduce biased gendered economic role attribution in image‑text memes. By assessing whether sufficient evidence exists for gendered reasoning, the model gates inference, abstaining when cues are ambiguous and confidently attributing roles when explicit. Evaluation on the EconMeme‑GE dataset shows CGER‑Net cuts gender overcommitment by up to 44% on ambiguous memes while preserving accuracy on clear cases, and human judges find 79% of its rationales epistemically aligned with the evidence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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