Smart recommendation algorithms have revolutionized information dissemination, enhancing efficiency and reshaping content delivery across various domains. However, concerns about user agency have arisen due to the inherent opacity (information asymmetry) and the nature of one-way output (power asymmetry) on algorithms. While both issues have been criticized by scholars via advocating explainable AI (XAI) and human-AI collaborative decision-making (HACD), few research evaluates their integrated effects on users, and few HACD discussions in recommender systems beyond improving and filtering the results. This study proposes an incubating idea as a missing step in HACD that allows users to control the degrees of AI-recommended content. Then, we integrate it with existing XAI to a flow prototype aimed at assessing the enhancement of user agency. We seek to understand how types of agency impact user perception and experience, and bring empirical evidence to refine the guidelines and designs for human-AI interactive systems.

翻译：智能推荐算法已彻底改变了信息传播方式，在多个领域中提升了效率并重塑了内容递送模式。然而，由于算法固有的不透明性（信息不对称）以及单向输出的特性（权力不对称），用户能动性问题随之凸显。尽管学者们通过倡导可解释人工智能和人类-人工智能协作决策来批评这两个问题，但鲜有研究评估其综合效应对用户的影响，且在推荐系统中针对HACD的讨论多局限于结果优化与筛选。本研究提出一个作为HACD缺失环节的孵化理念，允许用户控制AI推荐内容的程度。进而将这一理念与现有XAI技术整合，构建了一个旨在评估用户能动性增强效果的流程原型。我们试图探究不同类型的能动性如何影响用户的感知与体验，并通过实证证据为人机交互系统的设计指南与规范完善提供依据。