This preprint proposes a conceptual reframing of “error” in complex human–AI systems. Rather than treating error as an objective deviation from a correct outcome, the paper argues that error is a classification arising from perspective-dependent evaluative frames, system boundaries, and temporal horizons. In contemporary AI safety, reliability, and governance discourse, error is commonly modeled as a localizable event to be minimized through convergence, optimization, and control. While effective in deterministic or narrowly scoped domains, this framing becomes inadequate—and potentially harmful—in complex, irreversible, and meaning-laden decision environments such as healthcare, education, and socio-technical governance. The paper develops an alternative framework in which error is understood as a signal within a broader coherence field rather than a binary failure condition. It examines how distributed causality, observer-dependent attribution, and temporal drift undermine single-fault explanations and root-cause reflexes. The central claim is that systemic collapse often occurs not due to the presence of error, but due to incoherent interpretive spaces that obscure causal structure and suppress perspective plurality. Instead of aiming for error-free systems, the framework emphasizes the design of decision spaces that can sustain divergent interpretations without loss of coherence. An appendix explicitly clarifies the epistemic scope, non-closure principles, and intended use of the framework, positioning it as an analytical and design-oriented contribution rather than an optimization or control mechanism. This work is intended for researchers and practitioners working at the intersection of human cognition, AI systems, and complex decision environments, offering a foundation for coherence-based analysis of failure, drift, and interpretive instability.
Gyula Járadi (Tue,) studied this question.