行政处罚案件中的算法证据识别

Juan Zhang
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引用次数: 0

摘要

在行政处罚领域,算法证据是指在政府自动决策的操作步骤中,通过既定算法得到的直接结果。随着人工智能技术的发展,这类证据的智能性将不断消失。与传统证据相比,算法证据具有较高的技术性和复杂性,且具有公权力机关的认可。在司法实践中,只有这类证据才会受到法律审查。法官往往以门外汉的身份回避对技术问题的推理,导致行政争议无法得到实质性解决。面对非现场执法,司法裁判应跳出原有的证据审查框架,在证据收集阶段确保证据不被误认,在证据收集阶段落实行政主体与技术主体的举证责任,在质证阶段根据行政行为的性质采取不同的认定标准,平衡司法审查效率与行政相对人的有效权益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Algorithmic Evidence in Administrative Punishment Cases
In the field of administrative punishment, algorithmic evidence is the immediate result obtained through the established algorithm in the operation steps of automated decision-making by the government. The intelligibility of this kind of evidence will continue to disappear with the development of artificial intelligence technology. Compared with traditional evidence, algorithmic evidence is highly technical and complicated, and it has the endorsement of public authorities. In judicial practice, only such evidence is reviewed legally. Judges often evade reasoning on technical issues as laymen, resulting in administrative disputes that cannot be substantially resolved. In the face of off-site law enforcement, judicial decisions should jump out of the original evidence review framework, ensure that evidence is not misidentified in the evidence collection stage, implement the burden of proof of administrative subjects and technical subjects in the evidence collection stage, and adopt different identification standards according to the nature of administrative acts in the cross-examination stage, to balance the efficiency of judicial review and the effective rights and interests of administrative counterparts.
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