The silent accumulation: AI as mental contaminant

This paper introduces a theoretical framework for understanding how seemingly innocuous AI systems create cumulative effects on human cognition, emotion, and agency that current governance approaches fail to address. Drawing on environmental health science, we propose that "low-risk" AI applications, those falling below regulatory thresholds in frameworks such as the EU AI Act, function as cognitive environmental contaminants whose collective and sustained presence may reshape human psychological capacities. We operationalise cumulative AI exposure along five dimensions (frequency, duration, intensity, diversity of systems, and developmental timing) and identify five pathways through which cumulative effects may manifest: attention erosion, emotional dependency, social connection alteration, decision-making dependency, and identity fragmentation. For each pathway, we distinguish empirical regularities documented in existing research, plausible mechanisms through which cumulative effects may operate, and speculative population-level hypotheses that require empirical testing. Situating our framework against adjacent literatures including technostress, cognitive offloading, hypernudging, and automation bias, we argue that the distinctive contribution lies in the cumulative, cross-system, population-level analytical paradigm and its governance translation. We propose three governance mechanisms-cumulative impact assessment extending existing algorithmic auditing frameworks, cognitive-social environmental monitoring using validated psychometric instruments, and economic valuation of cognitive-social ecosystem services, accompanied by a phased validation strategy. The framework is offered as a complement to existing risk-based governance, addressing a specific gap: the systematic invisibility of effects that emerge from the interaction of multiple AI systems over extended time periods.