人工智能司法应用中的算法决策风险及其本土防范研究

Research on Algorithmic Decision-Making Risks in the Judicial Application of Artificial Intelligence and Its Local Prevention

摘要: 在司法数字化转型的浪潮中，人工智能算法正深度融入司法实践的核心环节。其在裁判辅助、风险评估、流程优化等领域的规模化应用，不仅显著提升了审判效率，更通过标准化推理模型推动裁判结果趋向统一，为智慧法院建设注入了技术动能。然而，技术赋能的背后潜藏着多重风险，对司法公正构成新的挑战。控辩双方在算法运用能力上的悬殊差距，可能引发诉讼资源分配失衡，导致程序正义的形式平等受损；司法数据的分散化存储与非标准化采集，直接造成算法模型训练数据质量不足，使得偏差性裁判时有发生；算法“黑箱”的不可解释性，既削弱了司法裁判的说服力，也为责任追溯设置了技术障碍；而过度依赖自动化处理，则可能稀释司法程序的人文价值，动摇程序正义的核心地位。基于本土司法实践需求和我国司法实践的特殊性需求，亟需构建系统化的风险防范体系。研究提出强化算法辅助定位、优化司法数据审查与采集、建立分层责任制度及推进法律价值数据化的防范体系，这一防范体系的构建，旨在为平衡技术赋能与正义维护的矛盾提供理论支撑，为智慧法院的可持续发展提供实践范本，以此为智慧法院建立过程中的技术赋能同维护正义之间矛盾解决工作给予理论支撑和参考范本。

Abstract: Amid the wave of digital transformation in the judicial field, artificial intelligence algorithms are deeply integrated into the core links of judicial practice. Their large-scale application in areas such as judicial adjudication assistance, risk assessment and process optimization has not only greatly improved trial efficiency, but also advanced the unification of adjudication results through standardized reasoning models, injecting technological momentum into the development of smart courts. Nevertheless, multiple hidden risks lie behind technological empowerment, posing new challenges to judicial justice. The huge gap between the prosecution and the defense in the capacity of algorithm application may lead to the imbalance in the distribution of litigation resources and undermine the formal equality of procedural justice. The decentralized storage and non-standard collection of judicial data directly result in poor quality of training data for algorithm models, giving rise to biased adjudication outcomes on a regular basis. The inexplicability of the algorithmic “black box” weakens the persuasiveness of judicial judgments and creates technical obstacles for accountability. Furthermore, excessive reliance on automated processing may dilute the humanistic value of judicial procedures and shake the core foundation of procedural justice. In response to the demands of localized judicial practice and the unique characteristics of China’s judicial system, it is urgent to establish a systematic risk prevention framework. This study proposes a prevention system covering clarifying the positioning of algorithm-assisted adjudication, optimizing the review and collection of judicial data, establishing a tiered accountability mechanism, and promoting the dataization of legal values. The construction of this system aims to provide theoretical support for balancing technological empowerment and justice safeguarding, offer practical models for the sustainable development of smart courts, and furnish theoretical references and practical examples for resolving conflicts between technological application and justice protection in the construction of smart courts.