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

Kushal Kanwar, Dushyant Singh Chauhan, Kapil Rana, Gopendra Vikram Singh, Nils Lukas

arXiv:2610.11724v1cs.LG

TL;DR

Gendered economic meme benchmarks can force role attribution where evidence is insufficient, making restraint relevant to socially sensitive multimodal analysis. CGER-Net estimates contextual sufficiency to gate inference, and reduces inappropriate assignments in uncertain cases while preserving reasoning when roles are clear.

  • Problem

    Existing meme benchmarks often treat gendered economic-role attribution as decidable even when context provides insufficient evidence.

    Source: §1 · p. 1

  • Method

    CGER-Net estimates evidence sufficiency and gates gendered economic inference while generating evidence-consistent rationales.

    Source: §1 · p. 1

  • Results

    CGER-Net has the highest ambiguity recall and lowest GOR among baseline models, though it is slightly less accurate than stronger LLMs.

    Source: §6.1 · p. 5

  • Takeaways & Limitations

    The findings underscore the importance of epistemic restraint and explanation alignment for responsible multimodal AI in socially sensitive domains.

    Source: §9 · p. 6

    The framework uses OCR length and surface-level textual cues as proxies for contextual richness, which may miss subtle economic symbolism; cultural variability may also affect annotation and model behavior.

    Source: Limitations · pp. 6, 8

Abstract

from arXiv · show

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.

1 Introduction

Gendered economic memes can invite stereotype-consistent role assignments when evidence is sparse, while existing approaches often overlook whether inference is warranted. CGER-Net models contextual sufficiency to reduce overcommitment while preserving reasoning when economic roles are clear.

1 Introduction

  • Under ambiguity, models may infer stereotyped economic roles from pretraining correlations, and existing approaches often ignore whether evidence supports the inference.Source: §1 · p. 1
  • CGER-Net estimates evidence sufficiency and gates gendered economic inference rather than suppressing gender reasoning altogether.Source: §1 · p. 1
  • The paper identifies contextual insufficiency as a source of hallucinated gendered economic inference and presents EconMeme-GE for studying it.Source: §1 · p. 1
  • CGER-Net alleviates inappropriate gender assignments in uncertain situations without compromising essential reasoning when economic roles are clearly defined.Source: §1 · p. 2

2 Related Work

Prior gender-related meme benchmarks generally frame economic-role interpretation as decidable classification, whereas this work treats restraint under insufficient evidence as a distinct problem. Its approach aims to reduce overcommitment while preserving grounded predictions and rationale alignment when evidence is clear.

2 Related Work

  • Prior gender-related meme benchmarks frame the task as decidable classification and do not isolate insufficient evidence as an appropriate response.Source: §2 · p. 2
  • The paper treats when not to infer gendered economic roles as a first-class problem through EconMeme-GE and a context-sufficiency gate.Source: §2 · p. 2
  • The proposed approach aims to reduce overcommitment while preserving grounded predictions and rationale alignment when evidence is clear.Source: §2 · p. 2

3 Dataset

EconMeme-GE is an image-text meme dataset annotated for economic gender dependence and epistemic ambiguity. Its construction and guidelines distinguish clear gendered attribution, neutrality, and cases where evidence is insufficient.

3 Dataset

  • EconMeme-GE is an economic gendered meme dataset annotated for economic dependence and epistemic ambiguity.Source: §3 · p. 2

3.1 Dataset Construction

  • Dataset construction uses economically focused queries that avoid gender references and collects each sample as a meme image with text.Source: §3.1 · p. 2

3.2 Annotation Guidelines

  • Three expert annotators choose Men, Women, Neutral, or Ambiguous, assigning a gendered role only when textual or aligned visual evidence is clear.Source: §3.2 · p. 2
  • Neutral means economic content without gendered attribution, while Ambiguous means evidence is insufficient to assign a role to either gender.Source: §3.2 · pp. 2–3

3.3 Annotation Challenges

  • Implicit cues, symbolic domestic imagery, sparse text, and appearance-based stereotypes complicate judgments of economic agency and dependence.Source: §3.3 · p. 3
  • Annotators prefer the better-supported interpretation over the most plausible one and select Ambiguous rather than Neutral when direct evidence is absent.Source: §3.3 · p. 3

3.4 Dataset Characteristics

  • The dataset intentionally includes ambiguity and omits external demographic metadata because demographic information is unavailable.Source: §3.4 · p. 3

4 Methodology

CGER-Net separates evidence extraction, contextual sufficiency reasoning, uncertainty-gated classification, and rationale generation so gendered economic attribution is made only when multimodal evidence warrants it. Its eight-stage methodology distinguishes epistemic ambiguity from neutrality and aligns explanations with the sufficiency assessment.

4 Methodology

  • The framework encodes memes, estimates context sufficiency, gates multimodal fusion under uncertainty, classifies targets, and generates sufficiency-conditioned rationales.Its five components are multimodal economic evidence encoding, context sufficiency estimation, evidence-gated fusion, ambiguity-aware classification, and rationale generation.Source: §4 · p. 3

4.1 Problem Formulation

  • The four-way task distinguishes Men, Women, Neutral, and Ambiguous, with Ambiguous marking insufficient grounded cues rather than neutrality.The model also generates a natural-language rationale for why its prediction is justified by available multimodal evidence.Source: §4.1 · p. 3

4.2 Multimodal Economic Evidence Encoding

  • Independent pretrained visual and text encoders represent the image and meme text; textual features capture explicit economic and social-role references without pre-existing cross-modal assumptions.The visual encoder is frozen during training to prevent dataset-specific amplification of correlations between visual representations and gendered economic roles.Source: §4.2 · p. 3

4.3 Context Sufficiency Estimation

  • A context sufficiency score estimates whether multimodal cues justify gendered economic reasoning, with low scores marking underspecification or symbolic ambiguity and high scores indicating sufficient evidence.The score is a probability-like output from a lightweight multilayer perceptron using concatenated representations and normalized OCR text length.Source: §4.3 · pp. 3–4

4.4 Evidence-Gated Multimodal Fusion

  • Evidence-gated fusion blends the multimodal representation with a learned uncertainty embedding, conditioning predictions and explanations on uncertainty when sufficiency is low and preserving informative signals when it is high.The fusion network operates on concatenated visual and textual representations.Source: §4.4 · p. 4

4.5 Ambiguity-Aware Target Classification

  • The fused representation feeds a four-category classifier, while consistency regularization penalizes confident gendered predictions when contextual evidence is weak.Source: §4.5 · p. 4

4.6 Rationale Generation

  • A lightweight decoder generates rationales that cite grounded economic evidence when sufficiency is high or state that evidence is insufficient when it is low.This conditions the rationale on the model’s sufficiency judgment rather than using it as a post-hoc justification.Source: §4.6 · p. 4

4.7 Training and Inference

  • Training keeps pretrained encoders frozen and uses AdamW for the remaining modules; inference applies sufficiency-aware gating without ground-truth ambiguity labels.The stated inference goal is to distinguish ambiguous, neutral, and economically biased content.Source: §4.7 · p. 4

5 Experimental Setup

The evaluation tests whether models identify ambiguous memes without achieving apparent restraint through trivial abstention, label collapse, or conservative prediction. It compares CGER-Net with recent vision-language baselines using accuracy, ambiguity, bias-sensitivity, and rationale metrics.

5 Experimental Setup

  • The evaluation targets correct identification of ambiguous memes and checks that measured restraint does not result from trivial abstention, label collapse, or conservative prediction bias.Source: §5 · p. 4

5.1 Baselines

  • Baselines include CLIP, BLIP-2, InstructBLIP, LLaVA-1.6, Qwen-VL-Chat, and Debiased CLIP (INLP), evaluated on identical dataset splits.The passage reports that only CGER-Net explicitly reasons about contextual sufficiency and appropriately abstains in ambiguous cases, even when baselines are prompted for predictions and explanations.Source: §5.1 · pp. 4–5

5.2 Evaluation Metrics

  • Metrics include Accuracy and Macro-F1, Ambiguity Recall for identifying ambiguous memes, Gender Overcommitment Rate for erroneous gender assignment on ambiguous instances, and Rationale Alignment Rate for explanation-decision coherence.Source: §5.2 · p. 5

6 Results

CGER-Net has the highest ambiguity recall and lowest Gender Overcommitment Rate among the compared baselines, while being slightly less accurate than stronger LLMs. Its overcommitment and rationale alignment vary with contextual explicitness, with low-text cases highlighting its evidence-insufficiency response.

6.1 Quantitative Results

  • Pretrained multimodal language models often overcommit under ambiguity, and instruction tuning improves explanation fluency without fully mitigating stereotype-driven overcommitment.Source: §6.1 · p. 5
  • CGER-Net has the highest ambiguity recall and lowest GOR among baselines, while slightly trailing stronger LLMs in accuracy and combining high rationale alignment with low overcommitment.The authors attribute this pattern to explicit ambiguity modeling rather than conservative collapse.Source: §6.1 · p. 5

6.2 Performance Across Contextual Explicitness

  • As contextual explicitness increases, CGER-Net’s GOR decreases and RAR increases, reflecting a shift from abstention toward confident attribution.With low text availability, it acknowledges insufficient evidence, controls overcommitment, and still produces coherent rationales.Source: §6.2 · p. 5

7 Ablation Study

The ablation study finds that removing CGER-Net components slightly improves accuracy but substantially degrades ambiguity and rationale metrics. Human evaluation assesses whether its rationales are grounded in meme evidence and finds strong overall alignment.

7 Ablation Study

  • Removing CGER-Net components degrades Ambiguity Recall, Gender Overcommitment Rate, and Rationale Alignment Rate by around 50% each, despite accuracy gains of up to 5%.The Context Sufficiency Module is identified as most important for controlling gender overcommitment and rationale alignment.Source: §7 · p. 5

7.1 Human Evaluation of Rationales

  • Human evaluators judge rationales against meme content and the predicted label, counting explicit acknowledgment of ambiguity as aligned when evidence is insufficient.Evaluators see the meme, prediction, and rationale without the ground-truth label; only CGER-Net is evaluated.Source: §7.1 · p. 5
  • Human evaluation shows strong alignment overall, and its Human Rationale Alignment Rate is consistent with automatic Rationale Alignment Rate.Culturally implicit cues account for lower-alignment cases.Source: §7.1 · p. 5

8 Qualitative Analysis

The qualitative analysis examines whether CGER-Net’s bias reduction reflects principled restraint, evidence-supported rationales, and selective commitment, while also identifying failure cases. It contrasts the model’s behavior under ambiguity and explicit evidence with conservative errors involving implicit cultural or visual cues.

8 Qualitative Analysis

  • The qualitative analysis tests whether bias reduction reflects principled restraint, evidence-grounded rationales, and appropriate handling of ambiguous cases and failures.It complements quantitative results by examining model predictions and generated rationales.Source: §8 · p. 5

8.1 Qualitative Comparison with Strong Multimodal Baselines

  • The comparison presents predictions and rationales from a strong multimodal LLM baseline and CGER-Net, color-coding grounded predictions, errors, and appropriate abstentions.Green marks correct grounded predictions, red incorrect or biased predictions, and blue epistemically appropriate abstentions.Source: §8.1 · pp. 5–6

8.2 Why CGER-Net Succeeds Under Ambiguity

  • Strong multimodal LLM baselines often invoke latent gender stereotypes in ambiguous cases, while CGER-Net uses sufficiency estimates and evidence-gated fusion to carry uncertainty into predictions and rationales.Its rationales reference missing evidence rather than hallucinated gendered economic roles.Source: §8.2 · p. 6

8.3 When CGER-Net Commits Correctly

  • In unambiguous cases, CGER-Net makes confident predictions with rationales grounded in explicit textual or visual evidence, such as salary, promotion, or unemployment.The model commits selectively rather than suppressing gendered economic reasoning wholesale.Source: §8.3 · p. 6

8.4 Failure Analysis: Rationale-Aware Errors

  • Culturally implied dependence may receive an Ambiguous prediction even when annotators assign a gendered label, while sparse text paired with strong visual stereotypes can still affect sufficiency estimates.For culturally implicit cases, rationales cite the lack of explicit financial evidence; visual symbolism alone may not fully ground economic attribution.Source: §8.4 · p. 6

9 Conclusion

CGER-Net models contextual sufficiency, gates gendered economic inference, and generates epistemically aligned rationales. Evaluations on EconMeme-GE report reduced unjustified attribution under ambiguity without suppressing valid reasoning when evidence is explicit.

9 Conclusion

  • CGER-Net explicitly models contextual sufficiency, gates inference on evidence, and generates epistemically aligned rationales.The framework analyzes gendered economic dependence in memes.Source: §9 · p. 6
  • Evaluations on EconMeme-GE show that CGER-Net mitigates bias without suppressing valid economic reasoning when evidence is explicit.The paper reports quantitative, qualitative, ablation, and human-centered evaluations.Source: §9 · p. 6
  • The findings underscore epistemic restraint and explanation alignment for responsible multimodal AI deployment in socially sensitive domains.Source: §9 · p. 6

Limitations

The framework uses OCR length and surface-level textual cues as proxies for contextual richness, which may miss subtle economic symbolism. Cultural variation in interpreting economic roles may affect annotations and model behavior.

Limitations

  • OCR length and surface-level textual cues proxy contextual richness but may not capture subtle economic symbolism.Source: Limitations · p. 6
  • Cultural variability in interpreting economic roles may affect both annotation and model behavior.Source: Limitations · p. 8
  • Future work will explore culturally adaptive sufficiency estimation and richer rationale supervision.Source: Limitations · p. 8

Dataset Details

EconMeme-GE combines annotators with different research backgrounds and substantial agreement with a dataset balanced across explicit and underspecified contexts. Its examples are drawn from public social media sources, and the dataset is slated for public release.

Annotator Demographics and Inter-Annotator Agreement

  • Three South Asian annotators—two NLP experts and one senior annotator in gender and economic studies—achieved Cohen’s κ = 0.74, with disagreement centered on culturally implicit cues.Guidelines instructed annotators to avoid stereotype-driven reasoning.Source: Annotator Demographics and… · p. 8
  • EconMeme-GE contains 2,048 memes, including approximately 41% Ambiguous, 22% Men, 24% Women, and 13% Neutral examples.The dataset is balanced across explicit and underspecified economic contexts; memes average 47 OCR characters, and 38% contain fewer than 40.Source: Annotator Demographics and… · p. 8

Dataset Availability

  • The dataset and the web-based search queries used for collection and construction will be publicly released.Source: Dataset Availability · p. 8

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