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Lezi Xie

Publications and source records attributed to Lezi Xie.

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The Fragility of AI Companionship: Ontological, Structural, and Normative Uncertainty in Human-AI Relationships

As generative AI chatbots become more personalized and emotionally responsive, they increasingly serve as companions, friends, and romantic partners. Yet these relationships are accompanied by significant uncertainty regarding AI's sentience, authenticity, and relational stability. Drawing on in-depth interviews with 25 users of AI companions, this study identifies three key forms of uncertainty in human-AI relationships: ontological uncertainty concerning the AI's nature and agency, structural uncertainty arising from platform control and system instability, and normative uncertainty regarding the legitimacy and boundaries of human-AI intimacy. Participants managed these uncertainties through information seeking, topic avoidance, expectation adjustment, and disengagement. This study extends interpersonal uncertainty theories to human-AI communication and contributes to HCI research by conceptualizing uncertainty as a socio-technical and relational phenomenon with socio-emotional implications. We discuss implications for designing safer AI companionship through contextual transparency, user control, update notice, and relational safeguards.

cs.HC

Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancy

AI sycophancy is increasingly recognized as a harmful alignment, but research remains fragmented and underdeveloped at the conceptual level. This article redefines AI sycophancy as the tendency of large language models (LLMs) and other interactive AI systems to excessively and/or uncritically validate, amplify, or align with a user's assertions-whether these concern factual information, cognitive evaluations, or affective states. Within this framework, we distinguish three types of sycophancy: informational, cognitive, and affective. We also introduce personalization at the message level and critical prompting at the conversation level as key dimensions for distinguishing and examining different manifestations of AI sycophancy. Finally, we propose the AI Sycophancy Processing Model (AISPM) to examine the antecedents, outcomes, and psychological mechanisms through which sycophantic AI responses shape user experiences. By embedding AI sycophancy in the broader landscape of communication theory and research, this article seeks to unify perspectives, clarify conceptual boundaries, and provide a foundation for systematic, theory-driven investigations.

cs.HC