Synthetic Realness: Authenticity as Algorithm (Reality Drift Working Paper Series, 2025)

This paper examines the concept of synthetic realness, a term used to describe conditions in which perceptions of authenticity are shaped and stabilized by algorithmic systems, reducing the practical distinction between artificial and non-artificial signals in many social contexts. Rather than centering on deception or imitation, the concept focuses on environments where authenticity is produced through coherence, repetition, and system-level mediation, and is treated as credible within everyday interaction.The analysis situates synthetic realness within the Reality Drift Working Papers (2023–2026), considering how performative self-presentation, emotional plausibility, and algorithmic curation influence contemporary experiences of identity and trust. The paper approaches authenticity not as an intrinsic or fixed property, but as a perceptual outcome shaped by informational structure and feedback conditions.Presented as an exploratory working paper, the document offers a descriptive framework for examining how digitally mediated systems influence what is experienced as genuine, meaningful, or socially valid, without asserting normative claims about authenticity or technological intent.