Recommended Selves: Authenticity and Algorithmic Filtering

[2608.14602] Recommended Selves: Authenticity and Algorithmic Filtering Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computers and Society arXiv:2608.14602 (cs) [Submitted on 2 Jul 2026] Title: Recommended Selves: Authenticity and Algorithmic Filtering Authors: Etienne Brown View a PDF of the paper titled Recommended Selves: Authenticity and Algorithmic Filtering, by Etienne Brown View PDF HTML (experimental) 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. Comments: 21 pages. Published in the Journal of the American Philosophical Association 12(1): 15-34 (2026). DOI: https://doi.org/10.1017/apa.2025.10009 Subjects: Computers and Society (cs.CY) ; Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR) Cite as: arXiv:2608.14602 [cs.CY] (or arXiv:2608.14602v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2608.14602 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Journal of the American Philosophical Association 12 (1): 15-34 (2026) Related DOI : https://doi.org/10.1017/apa.2025.10009 Focus to learn more DOI(s) linking to related resources Submission history From: Étienne Brown [ view email ] [v1] Thu, 2 Jul 2026 15:14:35 UTC (25 KB) Full-text links: Access Paper: View a PDF of the paper titled Recommended Selves: Authenticity and Algorithmic Filtering, by Etienne Brown View PDF HTML (experimental) TeX Source view license Current browse context: cs.CY < prev | next > new | recent | 2026-08 Change to browse by: cs cs.HC cs.IR References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• CosyVerif: an Online Verification Platform#312059
• Machine Learning (In) Security: A Stream of Problems#328794
• Algorithmic neutrality#85814
• Algorithms, Agency, and Respect for Persons#764326
• Computational Antitrust First Annual Conference: Exploring Antitrust 3.0#1178281