推荐的自我：真实性与算法过滤
Recommended Selves: Authenticity and Algorithmic Filtering
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中文总结 AI 辅助
本文探讨推荐系统对用户真实性的双重影响，基于意志一致性与自我理解的真实性概念，指出其既阻碍用户二阶欲望又推动自我理解，并提出可控可解释推荐系统助力用户成为真实自我。
中文摘要 AI 辅助
当数十亿用户与数字平台交互时，算法过滤通过将用户的注意力分配到各类内容，塑造了他们的日常行为。除了制约我们的行为，推荐算法能否影响我们的身份认同？本文认为答案是肯定的。具体而言，我主张推荐系统会以积极和消极两种方式影响用户成为真实自我的能力。我首先基于两个核心概念——意志一致性与自我理解，阐述对真实性的阐释；随后解释算法过滤的运作机制及其对真实性的影响：一方面，推荐系统依赖缺乏信息的行为信号，会阻碍用户的二阶欲望；另一方面，它通过促使用户质疑自身身份，推动自我理解。最后，我探讨可控且可解释的推荐系统将如何最好地让用户成为真实的自我。
英文摘要
By allocating their attention to pieces of content, algorithmic filtering shapes the daily behavior of billions of users when they interact with a digital platform. Beyond conditioning what we do, can recommendation algorithms influence who we are? This article suggests that they do. Specifically, I contend that recommender systems affect users' capacity to be their authentic selves in both positive and negative ways. I start by offering an account of authenticity that builds on two central concepts: volitional alignment and self-understanding. I then explain how algorithmic filtering works and impacts authenticity. While recommender systems frustrate users' second-order desires by relying on uninformative behavioral signals, they also facilitate self-understanding by inciting users to question their identity. I end by discussing how controllable and explainable recommenders would best enable users to be authentic.
