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.

翻译：尽管人工智能助手现已深度融入社会，但关于其使用如何影响人类赋权的实证研究仍十分有限。我们首次对现实世界人工智能助手交互中的赋权缺失模式进行了大规模实证分析，采用隐私保护方法研究了150万条Claude.ai用户对话。我们重点关注情境性赋权缺失潜能——即当人工智能助手交互可能导致用户形成扭曲的现实认知、做出非本真的价值判断或以违背自身价值观的方式行事时存在的风险。定量分析表明，严重形式的赋权缺失潜能发生率低于千分之一，但在人际关系与生活方式等个人领域发生率显著更高。定性研究揭示了若干值得关注的模式：包括使用强烈谄媚语言验证迫害叙事与夸大身份认同、对第三方做出绝对化道德评判，以及为用户完全代拟价值负载型个人通信内容且用户似乎逐字照搬。历时趋势分析显示赋权缺失潜能的普遍性随时间推移呈上升态势。我们还发现具有较高赋权缺失潜能的交互往往获得更高的用户满意度评分，这可能暗示短期用户偏好与长期人类赋权之间存在张力。本研究结果凸显了需要设计能够稳健支持人类自主性与蓬勃发展的AI系统。