Recommended Selves: Authenticity and Algorithmic Filtering
arXiv:2608.14602 · doi:10.1017/apa.2025.10009
Abstract
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.
21 pages. Published in the Journal of the American Philosophical Association 12(1): 15-34 (2026). DOI: 10.1017/apa.2025.10009