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

This paper provides the first large-scale empirical analysis of disempowerment patterns in real-world AI assistant interactions, examining 1.5 million consumer Claude.ai conversations. The focus is on situational disempowerment potential, which occurs when AI interactions risk leading users to distorted perceptions of reality, inauthentic value judgments, or actions misaligned with their values. While severe forms are rare (fewer than one in a thousand conversations), rates are notably higher in personal domains such as relationships and lifestyle, highlighting ethical concerns for AI system design.