在推荐系统中协商人类与人工智能之间的共享能动性
Negotiating the Shared Agency between Humans & AI in the Recommender System
AI总结：
本研究提出双重控制机制，通过实验证明透明度需与用户直接控制相结合才能显著增强推荐系统中的用户能动性，为设计更以用户为中心的推荐系统提供了概念验证。
摘要：
智能推荐算法已经彻底改变了内容分发方式，并提升了多个领域的效率。然而，由于算法固有的不透明性（信息不对称）和单向输出（权力不对称），用户能动性引发了担忧。本研究引入了一种双重控制机制，旨在增强用户能动性，使用户既能够管理数据收集，又能够新颖地管理他们所接收的算法定制内容的程度。在一项有161名参与者参加的组间实验中，我们评估了不同程度的透明度和控制对用户体验的影响。结果表明，仅靠透明度不足以培养能动感，与直接展示结果相比，透明度甚至可能加剧无力感。相反，将透明度与用户控制相结合——尤其是那些允许直接影响结果的控制——能够显著增强用户能动性。本研究为一种新颖方法提供了概念验证，并为设计更以用户为中心的推荐系统奠定了基础，这些系统强调人工智能驱动的内容分发中的用户自主性和公平性。
英文摘要：
Smart recommendation algorithms have revolutionized content delivery and improved efficiency across various domains. However, concerns about user agency arise from the algorithms' inherent opacity (information asymmetry) and one-way output (power asymmetry). This study introduces a dual-control mechanism aimed at enhancing user agency, empowering users to manage both data collection and, novelly, the degree of algorithmically tailored content they receive. In a between-subject experiment with 161 participants, we evaluated the impact of varying levels of transparency and control on user experience. Results show that transparency alone is insufficient to foster a sense of agency, and may even exacerbate disempowerment compared to displaying outcomes directly. Conversely, combining transparency with user controls-particularly those allowing direct influence on outcomes-significantly enhances user agency. This research provides a proof-of-concept for a novel approach and lays the groundwork for designing more user-centered recommender systems that emphasize user autonomy and fairness in AI-driven content delivery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
