The Authenticity Paradox: The Threshold Model of Synthetic Authenticity

Authenticity is moving to the heart of communication theory as an audience-level judgment built from cues. Current frameworks assume a human communicator and predict that richer channels enhance authenticity, an assumption we test by contrasting human and synthetic authenticity. We define synthetic authenticity as the perception that an artificial agent is real enough to engage despite known ontological hollowness. Drawing on a bibliometric map of 2,685 articles (1966–2025) and a meta-synthesis of 111 AI persona studies (2017–2025), we argue that human authenticity relies on cue accumulation, where redundancy reinforces trust, while synthetic authenticity relies on cue minimalism, where redundancy triggers scrutiny unless sensory fidelity is calibrated to the task. We operationalize this divergence through the Threshold Model of Synthetic Authenticity, which posits sufficiency, script, and relational thresholds beyond which the suspension of disbelief collapses and human-AI interaction shifts from communication to forensic audit.