Aesthetic Alignment Risks Assimilation: How Image Generation and Reward Models Reinforce Beauty Bias and Ideological "Censorship"
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. We find 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.
6940cc9965f1e24a1177ff80
Wenqi Marshall Guo
claimed_verified
2025-12-16T09:47:38.442Z
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Marshall Guo
weathon
user
weathon
6940cc9965f1e24a1177ff81
Qingyun Qian
claimed_verified
2025-12-17T10:09:27.248Z
673ee0c47a6896f51f7823f8
/avatars/19c93fe375e36489342129fbe221970d.svg
Q
TriangleMesh
user
TriangleMesh
6940cc9965f1e24a1177ff82
Khalad Hasan
6940cc9965f1e24a1177ff83
Shan Du
Aesthetic Alignment Risks Assimilation: How Image Generation and Reward Models Reinforce Beauty Bias and Ideological "Censorship"
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. We find 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.
State-of-the-art image generation and reward models exhibit bias towards conventional aesthetics, often failing to produce anti-aesthetic images as requested, thus compromising user autonomy and aesthetic diversity.
aesthetic preference anti-aesthetic outputs aesthetic-aligned generation models reward models image-to-image editing abstract artworks