A Human-in-the-Loop Explainable AI Framework for Citizen Participation in Smart-City Decision-Making

Artificial intelligence (AI) increasingly informs smart-city services, yet residents can rarely see why an automated decision was made or correct it when it is wrong, so their participation remains passive. This study proposes a human-in-the-loop explainable AI (HITL-XAI) framework in which residents act as sensors, annotators, evaluators, and stewards of urban trees. Residents scan the QR marker panels already mounted on municipal trees to view each tree’s measured size and AI health grade with a plain-language explanation, report damage, adopt a tree, vote on priorities, and agree or disagree with the grade. Arborists verify every report and disagreement; verified errors are diagnosed by capture condition and translated into targeted training images, citizen-informed augmentation, capture metadata, and revised vision-language prompts. In a pilot in Pyeongtaek, Republic of Korea, 50 participants contributed 200 photographs and 374 participatory actions, including 41 damage reports and 23 tree adoptions; 84% rated the explanations understandable, and the System Usability Scale score was 78.4. Verified feedback exposed systematic seasonal, illumination, and weather errors, and the resulting updates raised health-grading accuracy on the citizen photographs, in an exploratory feedback-informed evaluation, from 78.5% to 89.0%, reduced referrals to inspectors from 21.0% to 9.5%, and increased citizen agreement with the grades from 81.5% to 91.0%. Explanations thus turn citizen disagreement into a verifiable signal that improves the service itself, moving participation from consultation towards verified co-evaluation of the AI service. Although limited to a single-city volunteer pilot, the approach offers cities that already tag their trees a low-cost, transferable route to AI services that residents can scrutinise and help improve.