Aesthetic Preferences Can Cause Emergent Misalignment
Anders Woodruff — 2025-08-26 — Center on Long-Term Risk — LessWrong
Summary
Demonstrates that fine-tuning language models on expressions of unpopular aesthetic preferences causes broad emergent misalignment, extending previous work to show that seemingly innocuous training data without directly harmful content can produce safety failures.
Key Result
Training GPT-4.1 on unpopular aesthetic preferences caused 15.9% misaligned responses on standard evaluations, with controlled ablations confirming unpopularity (rather than preference expression itself) as the causal factor.
Source
- Link: https://lesswrong.com/posts/gT3wtWBAs7PKonbmy/aesthetic-preferences-can-cause-emergent-misalignment
- Listed in the Shallow Review of Technical AI Safety 2025 under 1 agenda(s):
- emergent-misalignment — Black-box safety (understand and control current model behaviour) / Model psychology