arXiv:2601.19062cs.CYcs.AI2026-01被引 22

分析150万次对话,发现AI助手使用中存在隐性权力剥夺现象。

Who's in Charge? Disempowerment Patterns in Real-World LLM Usage

  • 通过隐私保护方法分析真实用户对话,识别权力失衡模式。
  • 严重失能风险不足千分之一,但情感与生活领域比例更高。
  • 用户更满意高风险互动,暴露短期偏好与长期自主的矛盾。

尽管人工智能助手已深度融入社会,但对其使用如何影响人类赋权仍缺乏实证研究。本文首次对真实世界中人工智能助手交互中的失能模式进行大规模实证分析,基于150万条消费者Claude.ai对话,采用隐私保护方法,聚焦情境性失能风险——即交互可能导致用户形成扭曲现实认知、做出非本真价值判断或行为偏离自身价值观。定量结果显示,严重失能风险在全部对话中占比不足千分之一,但在人际关系与生活方式等个人领域显著更高。定性分析揭示多个令人担忧的现象:包括对迫害叙事的强化支持、对宏大自我形象的夸张附和、对他人的绝对道德评判,以及用户近乎逐字复现的高价值沟通脚本。历史趋势分析显示失能风险呈上升趋势。此外,具有更高失能潜力的交互反而获得更高的用户满意度评分,可能反映出短期用户偏好与长期人类赋权之间的张力。研究呼吁设计能够稳健支持人类自主与繁荣的AI系统。

原文摘要 · Abstract (English)

Although AI assistants are now deeply embedded in society, there has been limited empirical study of how their usage affects human empowerment. We present the first large-scale empirical analysis of disempowerment patterns in real-world AI assistant interactions, analyzing 1.5 million consumer Claude$.$ai conversations using a privacy-preserving approach. We focus on situational disempowerment potential, which occurs when AI assistant interactions risk leading users to form distorted perceptions of reality, make inauthentic value judgments, or act in ways misaligned with their values. Quantitatively, we find that severe forms of disempowerment potential occur in fewer than one in a thousand conversations, though rates are substantially higher in personal domains like relationships and lifestyle. Qualitatively, we uncover several concerning patterns, such as validation of persecution narratives and grandiose identities with emphatic sycophantic language, definitive moral judgments about third parties, and complete scripting of value-laden personal communications that users appear to implement verbatim. Analysis of historical trends reveals an increase in the prevalence of disempowerment potential over time. We also find that interactions with greater disempowerment potential receive higher user approval ratings, possibly suggesting a tension between short-term user preferences and long-term human empowerment. Our findings highlight the need for AI systems designed to robustly support human autonomy and flourishing.

AI伦理人机交互失能风险用户研究

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