Aligning to Illusions: Choice Blindness in Human and AI Feedback
Reinforcement Learning from Human Feedback (RLHF) assumes annotator preferences reflect stable internal states. We challenge this through three experiments spanning the preference pipeline. In a human choice blindness study, 91% of surreptitiously swapped preferences go undetected, extending choice blindness to third-person evaluative comparison of unfamiliar text. Testing fifteen LLM judges as potential replacements, we find detection relies on shallow text matching rather than genuine self-monitoring: removing prior reasoning from context causes blindness to surge from near-zero to over 50%, while explicit social pressure induces near-universal compliance. In a dose-response experiment ...
| Authors | Wenbin Wu |
|---|---|
| Primary category | cs.CL |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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