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arXiv · 2610.13185

From Observation to Intervention: Identifiability of Latent User Bias in Human-AI Interaction

Lucas Biechy·Yuxiao Li·Zhonghao He·Tianyi Qiu
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Abstract

Alignment methods typically treat user behavior as a reliable proxy for underlying preferences. However, this assumption breaks down when users hold belief-dependent cognitive biases, risking irreversible ``confirmation traps'' and harmful feedback loops. In this paper, we formalize when an AI system can distinguish true user preferences from behavior shaped by cognitive bias. Modeling this interaction as a multi-armed bandit, we first prove a non-identifiability theorem: under passive observation, a static binary biased user produces action trajectory distributions strictly identical to an unbiased user in a transformed environment, forcing any action-only estimator to perform no better than random guessing. We then show that active preference elicitation breaks this observational equivalence. Specifically, we prove that an Observer applying a finite sequence of adversarial zero-reward interventions of length $m_δ$ makes the post-intervention action distributions arbitrarily separable. Our theoretical results suggest that robust alignment requires actively intervening to tell informed preferences from biases.

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Lucas Biechy, Yuxiao Li, Zhonghao He, Tianyi Qiu. 2026-10-09. From Observation to Intervention: Identifiability of Latent User Bias in Human-AI Interaction. https://arxiv.org/abs/2610.13185

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