Distributed Witnessing in Artificial Intelligence-Mediated Societies: A Conceptual Model for Historical Continuity

Abstract

Artificial intelligence increasingly mediates how observations are selected, summarized, linked, retrieved, interpreted, and disseminated at speeds and scales beyond human review. This development creates a continuity problem: information may remain available while the provenance, context, and evidential relationships needed for future understanding weaken. This conceptual paper introduces distributed witnessing as a model for analyzing how human actors, institutions, technical systems, and AI-mediated processes sustain or erode those relationships across time. The model distinguishes information preservation from continuity, understood as the capacity to evaluate, reinterpret, and build upon past observations. This capacity matters because societies cannot correct inherited errors, assign responsibility, or learn from experience without it. The framework is developed through an integrative conceptual synthesis of scholarship on archives and provenance, collective memory, historical interpretation, information infrastructures, accountability, and AI-mediated knowledge production. The resulting model defines continuity representations, differentiates evidential sources, and identifies six witness functions: preservation, transfer, comparison, provenance assessment, contextualization, and reinterpretation. Together, these elements reveal the relationships that AI systems inherit and may obscure when they transform heterogeneous evidence into apparently unified outputs. The model is analytical rather than implemented or empirically validated. Its proposed relationships can be tested through case studies, system design, and governance research.