# Recommended Selves: Authenticity and Algorithmic Filtering

arXiv: https://arxiv.org/abs/2608.14602 · HTML: https://mlsanity.com/paper/2608.14602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2026.

## Built on by (most cited first, 1 of 1)

- [Free Speech and Artificial Intelligence](https://mlsanity.com/paper/2608.28973/) (2026)

## Abstract (arXiv)

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.