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Recommended Selves: Authenticity and Algorithmic Filtering
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DOI：10.1017/apa.2025.10009.png)
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Abstract
En 中文
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.
Keywords:
Authenticity
Second-Order Volition
Self-Understanding
Artificial Intelligence
Recommender Systems
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1.1
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