AI Iterative Distortion Effect (AIDE): Defining a Cross-Modal Failure Mode in Chained AI Processing

This working paper introduces and defines the AI Iterative Distortion Effect (AIDE): a cross-modal, nonlinear failure mode in chained AI processing pipelines. When data is repeatedly compressed, summarised, or reprocessed by successive AI systems, cumulative distortion increases nonlinearly until fidelity degrades beyond recoverability. The paper presents a formal definition, distinguishes AIDE from model collapse and dataset drift, provides an evidence base, operationalises three detection indicators, proposes mitigation practices, and includes a research proposal for empirical validation. AIDE sits within a theoretical framework originated by this author alongside AI Meta-Bias and Progressive Regression. Concept first developed November 2025, first published December 2025, submitted March 2026.