首页|期刊导航|重庆科技大学学报(社会科学版)|生成式人工智能赋能预测性侦查的正当性证成与限度廓清

生成式人工智能赋能预测性侦查的正当性证成与限度廓清OA

Generative AI Empowers the Justification and Limitation of Predictive Investigation

中文摘要英文摘要

在新型经济犯罪、网络犯罪频发的时代背景下,传统侦查模式暴露出诸多困境,如侦查程序繁杂、成本高昂等,这导致其应对上述犯罪愈发乏力.基于对涉生成式人工智能应用的规范解读以及域外预测性警务实践的经验参考,笔者发现生成式人工智能具有嵌入预测性侦查的可行性与正当性.然而,在生成式人工智能赋能预测性侦查的实践中,已然存在诸如正当程序弱化、权力异化加剧以及技术风险失控等困境,而这些困境与立法表达模糊、侦查权力失范等因素不无关系.基于此,当前应强化生成式人工智能赋能预测性侦查的程序机制,同时围绕侦查主体、侦查对象、启动标准等方面细化具体适用规则,并形塑以算法备案、算法匿名化机制为核心的技术保障机制.

In the context of the frequent occurrence of new economic crimes and cybercrimes,traditional investigation modes have exposed many difficulties,such as complicated investigation procedures and high costs,leading to their weakness in dealing with the crimes above.Based on the interpretation of norms related to the application of generative AI and the empirical reference of extraterritorial predictive policing practice,it is feasible and legitimate for generative AI to be embedded with predictive investigation.However,the practice of generative AI empowering predictive investigation already has difficulties such as weakening of due process,intensification of power alienation,and loss of control of technological risks,which are not unrelated to ambiguous legislative expression and anomie of investigative power.Based on this,it is necessary to strengthen the procedural mechanism of generative AI empowering predictive investigation while refining specific applicable rules around the investigation subjects,investigation objects and initiation standards,and shaping a technical guarantee mechanism with algorithm filing and algorithm anonymization mechanism as the core.

曾德梅

广东司法警官职业学院,广州 510520

社会科学

生成式人工智能预测性侦查预测性警务算法备案算法匿名化

Generative AIpredictive investigationpredictive policingalgorithm filingalgorithm anonymization

《重庆科技大学学报(社会科学版)》 2026 (2)

74-84,11

广东省普通高校青年创新人才类项目"心理测试技术在监狱工作中的应用"(2019GWQNCX062)广东省普通高校特色创新类项目"数字化转型背景下警务特色虚拟仿真教学资源开发与应用"(2023WTSCX223).

10.19406/j.issn.2097-4523.2026.02.006

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