推荐算法既可能阻碍真实自我，也可能促进自我认知。
Recommended Selves: Authenticity and Algorithmic Filtering
- 基于意愿一致与自我认知构建真实性理论框架
- 算法依赖模糊行为信号，削弱用户深层意愿满足
- 通过挑战身份认知，反而激发用户反思自我
算法推荐系统通过分配用户注意力，深刻影响数十亿人日常数字行为。本文探讨推荐系统是否能塑造人的身份认同。提出一种基于‘意愿一致’与‘自我理解’的真实性理论，指出推荐算法虽因依赖非信息性行为信号而挫败用户第二层欲望，但也能通过引发身份质疑促进自我认知。最终建议，可控制且可解释的推荐机制最有助于用户实现真实自我。
原文摘要 · Abstract (English)
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.
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