AI RESEARCH
When a Zero-Shooter Cheats: Improving Age Estimation via Activation Steering
arXiv CS.LG
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ArXi:2605.17658v1 Announce Type: new Different age-related regulations have been proposed to protect minors from harmful content and interactions online. Automated age estimation is central to enforcing such regulations, and vision-language models (VLMs) achieve state-of-the-art performance on this task. However, we find that the zero-shot nature of VLM-based age estimation produces an unexpected side effect we call the identity shortcut: Instead of estimating age from visual features, VLMs tend to identify the depicted person and infer their age from memorized knowledge.