AI-Enabled Early-Warning and Decision Support for Environmentally Induced Cancer in E-Waste and Automobile-Recycling Communities: A Conceptual Public-Health Framework

Abstract

Communities near informal electronic-waste processing and automobile dismantling experience complex exposures whose carcinogenic consequences often appear years later. Disease registries therefore identify harm after much of the preventable exposure has occurred. This conceptual study develops an AI-enabled early-warning and decision-support framework designed to shift prevention upstream. The architecture contains four coupled layers. A sensing layer combines low-power environmental monitoring with targeted biomonitoring under explicit quality and provenance controls. A predictive-risk engine estimates biochemical and carcinogenic risk with calibrated uncertainty and flags emerging hotspots. A decision-support layer ranks source control, worker protection, advisories, surveillance, and remediation by expected risk reduction, cost, and feasibility. A governance-and-equity layer controls consent, privacy, access, non-discrimination, community participation, and source-reduction primacy. Framework development followed a decision-backward synthesis: prevention decisions were defined first, followed by the evidence, technical functions, safeguards, and failure conditions needed to support them. The proposal is falsifiable at each layer. Advancement requires adequate sensor coverage and reliability, external calibration, acceptable subgroup performance, uncertainty coverage, manageable alert burden, auditable recommendations, and demonstrated corrective action. Implementation should progress from a bounded pilot to 80 comparative decision evaluation and independent governance review. Success is defined by reduced exposure and biological risk, not predictive accuracy alone. The framework does not support autonomous enforcement or unrestricted individual surveillance. Its contribution is an integrated research architecture for testing whether continuous sensing and accountable prediction improve the timing, targeting, and equity of environmental-health protection in high-burden recycling communities.