Responsible AI for Monitoring Coral Reef Ecosystems

Abstract

Artificial intelligence is rapidly expanding the scale, frequency, and analytical capacity of coral reef monitoring through automated image classification, satellite remote sensing, autonomous underwater vehicles, acoustic sensing, photogrammetry, and predictive ecological modelling. These technologies are particularly relevant as coral reefs experience unprecedented thermal stress and increasingly complex interactions among bleaching, pollution, disease, overfishing, sedimentation, and habitat degradation. However, AI-supported environmental monitoring can itself produce ecological and governance risks when training datasets are geographically unrepresentative, classification uncertainty is hidden, local ecological knowledge is excluded, models are deployed beyond their validated domains, or automated alerts directly influence conservation interventions without expert verification. This study proposes a Responsible Reef Artificial Intelligence Governance Framework and evaluates its principles using a reproducible simulation of 2,400 synthetic underwater image segments distributed across healthy complex reefs, bleached coral, algal overgrowth, and turbid or low-light reef environments. Simulated F1 scores declined from 0.94 under healthy high-visibility conditions to 0.76 under turbid conditions. False ecological alert rates reached 18.4% in the most challenging condition under AI-only monitoring but declined to 6.0% following uncertainty thresholds, ecological expert validation, and contextual review. Across all conditions, responsible-AI controls reduced the modelled false-alert rate by approximately 67%. The findings demonstrate that responsible reef AI requires representative ecological datasets, uncertainty disclosure, human and community oversight, interoperable monitoring  standards, transparent model documentation, environmental sustainability, data sovereignty, and continuous field-based revalidation.