Detailed comparative analysis of methods.

Efficient screening of corruption allegations is crucial for promoting accountability and transparency in public administration. However, many institutions still rely on manual processes that are prone to inefficiency and inconsistency. As AI gains traction across sectors, this study develops and evaluates an artificial intelligence (AI)-powered prototype designed to support the preliminary screening of corruption complaints at Thailand’s National Anti-Corruption Commission (NACC). The proposed system integrates Optical Character Recognition (OCR), Natural Language Processing (NLP), and machine learning techniques to automate document handling and improve workflows. A mixed-methods research approach was adopted, combining institutional process analysis with a comprehensive technical performance assessment. The OCR module achieved an F1-score of 81.8%, with precision and recall of 84.2% and 79.6%, respectively. For printed text, the system attained 72% word-level accuracy and 78% at the character level. Additionally, the integrated framework demonstrated a classification accuracy of 57.5% and significantly improved operational efficiency, reducing average complaint processing time by 78.6% compared to traditional manual methods. The findings highlight AI’s transformative potential in enhancing anti-corruption efforts through increased speed, accuracy, and consistency. They underscore the importance of responsible and context-sensitive AI adoption in public sector governance. This study contributes to the growing discourse on digital governance by providing empirical evidence and practical insights for policymakers and practitioners aiming to implement scalable, transparent, and ethically grounded AI solutions within institutional accountability frameworks.

高效甄别腐败指控，对于提升公共行政领域的问责性与透明度至关重要。然而，诸多机构仍依赖人工处理流程，此类流程极易出现效率低下与标准不一的问题。随着人工智能（AI）在各行业的影响力日益提升，本研究开发并评估了一款人工智能赋能的原型系统，用于协助泰国国家反腐败委员会（National Anti-Corruption Commission, NACC）开展腐败投诉的初步甄别工作。该拟议系统整合了光学字符识别（Optical Character Recognition, OCR）、自然语言处理（Natural Language Processing, NLP）与机器学习技术，以实现文档处理自动化并优化工作流程。本研究采用混合研究方法，将机构流程分析与全面的技术性能评估相结合。该OCR模块的F1值达81.8%，精确率与召回率分别为84.2%与79.6%；针对印刷文本，系统的词级准确率达72%，字符级准确率达78%。此外，该整合框架的分类准确率达57.5%，且显著提升了运营效率：相较于传统人工流程，平均投诉处理时长缩短了78.6%。研究结果凸显了人工智能在强化反腐败工作中的变革性潜力，可通过提升处理速度、准确率与一致性实现这一目标，同时强调了在公共部门治理中，负责任且贴合情境地应用人工智能的重要性。本研究通过提供实证依据与实践洞见，为数字治理领域日益增长的学术话语作出贡献，可为旨在机构问责框架内部署可扩展、透明且符合伦理规范的人工智能解决方案的政策制定者与从业者提供参考。