探討人工智慧在偏遠地區警察勤務應用之挑戰與接受度──以科技接受模式（TAM）為例

An Analysis of the Challenges and Acceptance of AI Applications in Remote-Area Police Operations: Applying the Technology Acceptance Model (TAM)

人工智慧（Artificial Intelligence, AI）技術快速發展，已逐步融入警政實務與公共治理，成為推動智慧警政的重要動能。偏遠地區警察機關普遍面臨人力不足、巡邏範圍廣與應變能量有限等挑戰，AI展現出強化勤務與治安維護的潛力。實務中，AI已應用於行政值班、風險預警及資源整合，反映警政組織對科技導入的積極態度與策略規劃。
本研究以科技接受模式（Technology Acceptance Model, TAM）結合組織變革理論，採質性深度訪談法，分析警察與民眾對AI警政技術之知覺有用性、知覺易用性與接受態度。結果顯示，多數受訪者雖未實際操作系統，仍普遍肯定AI可降低行政負擔、提升執勤安全與決策效率；惟亦指出操作不便、制度支援與資料安全不足等問題，顯示AI導入不僅為技術革新，亦關涉組織治理與社會信任之再造。
綜合分析結果，建議強化AI教育訓練與數位素養、完善個資與倫理規範、推動資源整合與警民協作，並建立示範據點與持續評估機制，以促進科技創新與社會信任的均衡發展。

Artificial Intelligence (AI) technology has rapidly evolved and been integrated into policing practices and public governance, becoming a driving force behind smart policing. In remote areas, police agencies face challenges such as personnel shortages, wide patrol coverage, and limited response capacity. AI demonstrates strong potential to enhance operational efficiency and public safety. In practice, AI has been applied to administrative duty management, risk prediction, and interstation resource integration, reflecting police organizations’proactive approaches to technological innovation and strategic deployment.
This study employs the Technology Acceptance Model (TAM) combined with organizational change theory, using qualitative in-depth interviews to examine police officers’and citizens’perceptions of usefulness, ease of use, and acceptance toward AI policing applications. Findings indicate that although most respondents have little direct system experience, they generally recognize the benefits of AI in reducing administrative burden, improving safety, and optimizing decision-making. However, issues such as operational complexity, institutional support, and data security remain concerns, suggesting that AI implementation involves not only technological advancement but also organizational governance and trust reconstruction. Based on the findings, the study proposes enhancing AI-related training and digital literacy, strengthening data protection and ethical frameworks, promoting resource integration and police-community collaboration, and establishing demonstration projects with ongoing evaluation to achieve balanced development between innovation and social trust.