The recent proliferation of artificial intelligence (AI) gives rise to questions on how users interact with AI services and how algorithms embody the values of users. Despite the surging popularity of AI, how users evaluate algorithms, how people perceive algorithmic decisions, and how they relate to algorithmic functions remain largely unexplored. Invoking the idea of embodied cognition, we characterize core constructs of algorithms that drive the value of embodiment and conceptualizes these factors in reference to trust by examining how they influence the user experience of personalized recommendation algorithms. The findings elucidate the embodied cognitive processes involved in reasoning algorithmic characteristics – fairness, accountability, transparency, and explainability – with regard to their fundamental linkages with trust and ensuing behaviors. Users use a dual-process model, whereby a sense of trust built on a combination of normative values and performance-related qualities of algorithms. Embodied algorithmic characteristics are significantly linked to trust and performance expectancy. Heuristic and systematic processes through embodied cognition provide a concise guide to its conceptualization of AI experiences and interaction. The identified user cognitive processes provide information on a user’s cognitive functioning and patterns of behavior as well as a basis for subsequent metacognitive processes.

人工智能（AI）的近来激增引起了有关用户如何与AI服务交互以及算法如何体现用户价值的问题。尽管AI迅速普及，但用户如何评估算法，人们如何看待算法决策以及它们与算法功能的关系仍在很大程度上尚待探索。引用体现认知的思想，我们表征算法的核心构造，这些核心构造驱动体现的价值，并通过检查信任如何影响个性化推荐算法的用户体验来概念化这些因素以参考信任。研究结果阐明了推理算法特征（公平，负责，透明，和可解释性–与信任和随之而来的行为之间的基本联系。用户使用双流程模型，从而在规范值和与性能相关的算法质量的组合基础上建立信任感。体现的算法特征与信任度和性能期望显着相关。通过具体化的认知进行的启发式和系统化过程为其AI体验和交互的概念化提供了简洁的指南。所识别的用户认知过程提供有关用户认知功能和行为模式的信息，以及后续元认知过程的基础。体现的算法特征与信任度和性能期望显着相关。通过具体化的认知进行的启发式和系统化过程为其AI体验和交互的概念化提供了简洁的指南。所识别的用户认知过程提供有关用户认知功能和行为模式的信息，以及后续元认知过程的基础。体现的算法特征与信任度和性能期望显着相关。通过具体化的认知进行的启发式和系统化过程为其AI体验和交互的概念化提供了简洁的指南。所识别的用户认知过程提供有关用户认知功能和行为模式的信息，以及后续元认知过程的基础。