Making AI Agents Evaluate Misleading Charts without Nudging

Fuente: arXiv
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Autor principal: Panda, Swaroop
Formato: Preprint
Publicado: 2026
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author Panda, Swaroop
author_facet Panda, Swaroop
contents AI agents are increasingly used as low-cost proxies for early visualization evaluation. In an initial study of deliberately flawed charts, we test whether agents spontaneously penalise chart junk and misleading encodings without being prompted to look for errors. Using established scales (BeauVis and PREVis), the agent evaluated visualizations containing decorative clutter, manipulated axes, and distorted proportional cues. The ratings of aesthetic appeal and perceived readability often remained relatively high even when graphical integrity was compromised. These results suggest that un-nudged AI agent evaluation may underweight integrity-related defects unless such checks are explicitly elicited.
format Preprint
id arxiv_https___arxiv_org_abs_2602_05662
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Making AI Agents Evaluate Misleading Charts without Nudging
Panda, Swaroop
Human-Computer Interaction
AI agents are increasingly used as low-cost proxies for early visualization evaluation. In an initial study of deliberately flawed charts, we test whether agents spontaneously penalise chart junk and misleading encodings without being prompted to look for errors. Using established scales (BeauVis and PREVis), the agent evaluated visualizations containing decorative clutter, manipulated axes, and distorted proportional cues. The ratings of aesthetic appeal and perceived readability often remained relatively high even when graphical integrity was compromised. These results suggest that un-nudged AI agent evaluation may underweight integrity-related defects unless such checks are explicitly elicited.
title Making AI Agents Evaluate Misleading Charts without Nudging
topic Human-Computer Interaction
url https://arxiv.org/abs/2602.05662