<![CDATA[Trading through marketplaces expands public access to drugs and food, but it also increases the complexity of oversight because of the large volume of products, sellers, and digital information. This study aims to formulate an artificial intelligence (AI)-supported public participation model that strengthens risk-based drug and food oversight while maintaining the accountability of human regulators. The study employed a normative juridical method with statutory and conceptual approaches, supported by a review of previous research on digital public participation, artificial intelligence, risk-based oversight, and algorithmic accountability. The study produced the AI-Assisted Public Participation Model (AIPPM), which integrates public reporting, AI-based screening, risk prioritization, regulator verification, oversight action, and public feedback. The model positions the public as a source of regulatory intelligence, AI as an instrument that supports risk detection and prioritization, and human regulators as the final decision-makers. This integration has the potential to improve the reach, speed, and accuracy of oversight priorities in digital commerce without transferring regulatory authority and responsibility to algorithms, in line with the principle “AI detects and prioritizes; humans verify and decide”.]]>
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