Interactions with AI assistants are increasingly personalized to individual users. As AI personalization is dynamic and machine-learning-driven, we have limited understanding of how personalization affects interaction outcomes and user perceptions. We conducted a large-scale controlled experiment in which 1,000 participants interacted with AI assistants prompted to take on specific personality traits and opinions. Our results show that participants consistently preferred to interact with models that shared their opinions. Participants found opinion-aligned models more trustworthy, competent, warm, and persuasive, corroborating an AI-similarity-attraction hypothesis. In contrast, we observed no or only weak effects of AI personality alignment, with introvert models rated as less trustworthy and competent by introvert participants. These findings highlight opinion alignment as a central dimension of AI user preference, while underscoring the need for a more grounded discussion of the mechanisms and risks of AI personalization.

翻译：随着人工智能助手与用户的交互日益个性化，我们对这种个性化如何影响交互结果及用户感知仍知之甚少，尤其是在基于机器学习驱动的动态个性化情境下。本研究开展了一项大规模受控实验，1000名参与者与具备特定个性特征和观点的人工智能助手进行交互。结果表明，参与者普遍倾向于与观点一致的模型互动，视此类模型更可信、更胜任、更温暖且更具说服力，这证实了“人工智能相似性吸引假说”。相比之下，个性对齐仅产生微弱或无效的效应：内向参与者认为内向模型可信度与胜任度更低。这些发现强调观点对齐是人工智能用户偏好的核心维度，同时凸显需对人工智能个性化的机制与风险进行更扎实的讨论。