Children as Canaries: The Pediatric Sentinel Effect in Algorithmic Harm

Abstract

Children function as sentinel populations for algorithmic harm in the same way they serve as early indicators of environmental toxicity in public health research. This paper introduces the Pediatric Sentinel Effect in Algorithmic Harm, a framework proposing that harms experienced by children from AI and algorithmic systems reliably precede recognition of those same harms in adult populations. Drawing on analysis of 40 source documents (including UNICEF regional assessments, the UN Convention on the Rights of the Child, and the AI Incidents Database) comprising over 7,300 extracted harm terms, we identify three domains where the sentinel effect is empirically observable: biometric surveillance (school facial recognition deployment preceded adult wrongful arrest cases by eight months), predictive risk systems (child welfare algorithms established precedents for administrative data misuse now spreading to adult contexts), and recommender systems (documented mental health impacts on adolescents preceded clinical recognition of algorithmic addiction in adults). We propose three mechanisms driving this effect: mandatory institutional exposure reducing children's capacity to opt out, cognitive developmental factors increasing susceptibility to algorithmic manipulation, and paradoxical data vulnerability wherein the most legally protected population generates the most behavioral data. The framework has limitations in economic and labor domains where adults experience harms first. We argue that child-focused AI safety testing should be treated as a leading indicator methodology for population-level risk assessment, and that existing pediatric sentinel frameworks from toxicology and environmental health offer immediate methodological templates for AI governance.