Position: Universal Aesthetic Alignment Narrows Artistic Expression
Wenqi Guo, Qingyun Qian, Khalad Hasan, Shan Du
Abstract
Over-aligning image generation models to a generalized aesthetic preference conflicts with user intent, particularly when "anti-aesthetic" outputs are requested for artistic or critical purposes. This adherence prioritizes developer-centered values, compromising user autonomy and aesthetic pluralism. We test this bias by constructing a wide-spectrum aesthetics dataset and evaluating state-of-the-art generation and reward models. This position paper finds that aesthetic-aligned generation models frequently default to conventionally beautiful outputs, failing to respect instructions for low-quality or negative imagery. Crucially, reward models penalize anti-aesthetic images even when they perfectly match the explicit user prompt. We confirm this systemic bias through image-to-image editing and evaluation against real abstract artworks.
BibTeX
@inproceedings{icml2026_positionuniversa,
title = {Position: Universal Aesthetic Alignment Narrows Artistic Expression},
author = {Wenqi Guo and Qingyun Qian and Khalad Hasan and Shan Du},
booktitle = {ICML 2026},
year = {2026}
}