Post-Deployment Observability as a Foundation for Well-Being-Aligned Human–AI Co-Evolution

Abstract

Current discussions of human creativity and generative AI often focus on model capabilities at the point of release, framing outcomes in terms of augmentation versus replacement. In deployed settings, however, the effects of AI systems on human agency, creativity, and institutional well-being emerge over time, shaped by repeated interaction, reuse, and integration into real-world workflows. These dynamics are rarely visible through pre-deployment evaluation or isolated prompt–response analysis. This paper argues that post-deployment observability is a foundation for well-being-aligned human–AI co-evolution. We present a system-level framework for externalized behavioral monitoring that treats generative AI systems as participants in socio-technical ecosystems rather than static tools. The framework emphasizes interpretable, aggregate behavioral signals - such as shifts in output velocity, semantic and structural reuse, persistence of synthetic roles, and cross-context propagation - that emerge cumulatively through time. Rather than automating judgment or enforcement, these signals support human-in-the-loop interpretation, enabling earlier awareness of when AI use patterns may be drifting from creative augmentation toward automation pressure, authority substitution, or unintended displacement of human agency. By focusing on observation instead of prediction, and governance rather than control, the proposed approach complements existing alignment and safety practices while preserving human judgment, institutional choice, and long-term wellbeing.