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：智能推荐算法革新了信息传播方式，提升了效率并重塑了各领域的内容分发模式。然而，算法固有的不透明性（信息不对称）与单向输出特性（权力不对称）引发了关于用户代理权的担忧。尽管学界通过倡导可解释人工智能（XAI）与人机协同决策（HACD）对这两类问题进行了批判性探讨，但鲜有研究评估其综合效应，且推荐系统中关于HACD的讨论多局限于优化与筛选结果。本研究提出一种补充性构想——通过允许用户控制AI推荐内容的程度，弥补HACD中缺失的关键环节。进而将这一构想与现有XAI技术整合，构建评估用户代理权增强效果的流程原型。我们旨在探究不同代理类型如何影响用户感知与体验，为优化人机交互系统的设计准则与实施方案提供实证依据。