Human versus Computer Vision
arXiv:2608.10181v1 Announce Type: new Abstract: Computer vision saliency models predict where people will look, one map per image, and a billion-dollar predicted-attention industry sells those maps in place of measuring real viewers. I test the leading models from the audience side, against 11.4 million webcam gaze points from 3,023 US adults recruited to national quotas, viewing circulating news photographs. I show that an untrained central marker outperforms every trained network, because the content the networks add on top of the center falls where these audiences never look. What accuracy remains is systematically biased, favoring younger, White, and moderate viewers over older, Black, and ideologically extreme ones. I propose a way forward and build on what a group's own gaze reveals about whether a model can learn that group at all, and I apply it across every demographic axis this sample supports. Ultimately, I show how systems that decide what people see can learn to see everyone, and this study supplies the standard by which such a claim should be judged.
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[Submitted on 10 Aug 2026]
Title:Human versus Computer Vision
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Abstract:Computer vision saliency models predict where people will look, one map per image, and a billion-dollar predicted-attention industry sells those maps in place of measuring real viewers. I test the leading models from the audience side, against 11.4 million webcam gaze points from 3,023 US adults recruited to national quotas, viewing circulating news photographs. I show that an untrained central marker outperforms every trained network, because the content the networks add on top of the center falls where these audiences never look. What accuracy remains is systematically biased, favoring younger, White, and moderate viewers over older, Black, and ideologically extreme ones. I propose a way forward and build on what a group's own gaze reveals about whether a model can learn that group at all, and I apply it across every demographic axis this sample supports. Ultimately, I show how systems that decide what people see can learn to see everyone, and this study supplies the standard by which such a claim should be judged.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
Cite as: arXiv:2608.10181 [cs.CV]
(or arXiv:2608.10181v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.10181
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Elena Sirotkina [view email] [v1] Mon, 10 Aug 2026 19:53:24 UTC (5,182 KB)
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