Recommended Selves: Authenticity and Algorithmic Filtering

Research suggests that algorithmic filtering, a core component of digital platforms, influences users' authenticity by shaping their daily behavior and potentially their sense of self. This influence can manifest in both positive and negative ways, primarily through the allocation of user attention to content. The study defines authenticity based on volitional alignment and self-understanding, arguing that recommender systems can hinder users' higher-order desires due to reliance on superficial behavioral data, while also offering some facilitating aspects.

Why it matters

The pervasive nature of algorithmic filtering across digital platforms means its influence on user behavior and self-perception is a critical consideration. Understanding this impact is crucial for platform developers, policymakers, and any institution leveraging digital engagement, as it directly affects user experience, well-being, and potentially, societal norms and individual agency.

Key insights

• Algorithmic filtering, via content allocation, directly shapes the daily behavior of billions of digital platform users.
• Recommendation algorithms can influence users' capacity to be their authentic selves.
• Authenticity is conceptualized through the lens of volitional alignment and self-understanding.
• Recommender systems can negatively impact authenticity by frustrating users' second-order desires due to their reliance on uninformative behavioral signals.
• Algorithmic filtering also possesses mechanisms that can positively facilitate authenticity.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.14602