安徽大学法学院,合肥 230601
寿晓明:安徽大学法学院博士研究生。研究方向:竞争法、数字法。
D915.3
本文受江西省高校人文社会科学研究项目“去算法歧视的法理基础与规范展开研究”(项目编号:FX22107)的资助
School of Law, Anhui University, Hefei 230601 , China
在生成式人工智能技术实现规模化应用后,其生成的信息大量进入刑事侦查等场景,成为了案件裁判的重要素材。此类信息具有算法黑箱性、主体模糊性、内容创造性等特征,与以人类主导生成、客观事实记录、固定载体存储为要素的传统证据规则存在冲突,带来了证据能力审查、证明力评估、证据认定实操等困境。在证据能力层面,真实性审查遭遇来源资质无效、内容印证失灵的难题,关联性认定因溯源困难而陷入实质关联判断的僵局,合法性审查面临主体责任模糊、程序适配不足、内容合规风险并存的问题。在证明力层面,判断标准缺失、层级定位模糊,导致同案不同判的风险极大。在证据实操层面,证据保存的完整性难以保证,认证程序缺乏专门规则保障其可靠性。要破解上述困境,应从证据的三性原理出发,并衔接生成技术的特性,以技术可追溯性为锚点,构建底层逻辑、方法逻辑与价值逻辑的分析框架。在实践层面,基于生成方式和证据用途将生成信息类型化为辅助生成型与自主生成型、事实证明型与程序辅助型,分别构建证据能力三性审查、证明力差异化评估、实操流程适配等规则,并厘清举证责任分配、质证程序规范、完整性保存技术路径及专家辅助机制。
After the large-scale application of generative artificial intelligence technology, a large amount of information generated by it enters criminal investigation and other scenes, becoming an important material for case adjudication. This kind of information has the characteristics of black box algorithm, fuzzy subject and creative content, which conflicts with the traditional evidence rules with human dominant generation, objective fact record and fixed carrier storage as elements, thus bringing difficulties such as evidence ability examination, proof evaluation and evidence identification. At the level of evidence ability, authenticity examination encounters the difficult problems of invalid source qualification and failure of content verification; relevance identification falls into the deadlock of substantial association judgment due to difficulty in traceability; legality examination faces the problems of vague subject responsibility, insufficient procedure adaptation and coexistence of content compliance risks. At the level of probative power, the lack of judgment standard and vague hierarchical positioning lead to great risk of different judgments in the same case. At the practical level of evidence, it is difficult to guarantee the integrity of evidence preservation, and the authentication procedure lacks special rules to ensure its reliability. In order to solve the dilemma, we should start from the three principles of evidence, link up the characteristics of generative technology, and take the traceability of technology as the anchor point to construct the analysis framework of underlying logic, method logic and value logic. At the practical level, based on the generation mode and evidence use, the classification is carried out out: the auxiliary generation type is distinguished from the independent generation type, the fact proof type and the procedure auxiliary type, the rules such as the three-nature examination of evidence ability, the differential evaluation of proof power, the adaptation of practical operation process, etc. are constructed respectively, and the distribution of burden of proof, the standardization of cross-examination procedures, the technical path of integrity preservation and the expert auxiliary mechanism are clarified.
寿晓明.人工智能生成信息的刑事证据类型化认定[J].上海对外经贸大学学报,2026,33(4):97-109.
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