Conceptual analysis reveals how continuous AI interaction alters self-perception in digital consumers, suggesting risks of reduced personal autonomy.
Key Points
To theorize how ongoing human–AI interaction and computational mediation alter human self-concept, identity formation, and autonomy.
Developed a theoretical framework linking predictive-processing models of the self with continuous human–AI interactions.
Modeled identity shifts across predictive recommendation algorithms and large language model conversational agents.
Identified a three-stage mirroring mechanism where synthetic social realities and computational identity capture drive users to conform to machine pacing, logic, and legibility.
Differentiated algorithmic mirroring from human social looping by establishing that computational feedback strictly converges rather than presenting contestable social evidence.
Characterized the resulting drift toward 'robotoid humanness' as an autonomy risk driven by the normalization of reduced, machine-legible personhood.