利用人工智能和机器学习算法有效解决民事纠纷的前景

V. Turkanova
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引用次数: 0

摘要

本文考察了乌克兰现代司法模式形成的主要方向,包括可持续发展,支持和发展强大与和平机构的必要性,以及乌克兰立法对欧盟法律的适应。在对现有方法进行分析的基础上,利用人工智能开发工具,分析国家法院判决统一登记册的大型数据集,以识别司法系统运作的稳定规律,将有可能通过系统的开发和风险评估来实现民事案件的预期结果,增加资金奖励的百分比,确保国家司法经费的有效使用,促进司法事业在社会各阶层平等发展。研究表明,有必要确定司法系统有效运作的指标和影响法院有效审理民事案件的主要因素,以及法院诉讼程序时间过长、法院判决不执行和法院费用高的风险的原因。研究结果表明,有必要从分析的案例中编制信息目录,考虑民事诉讼程序的行政绩效指标,并确定其影响诉讼程序有效性的主要和重要因素,并推导定量和有效的指标。
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Prospects for the use of artificial intelligence and machine learning algorithms for effective resolution of civil disputes
The article examines the main direction of the modern model’s formation of justice in Ukraine regarding sustainable development, the need to support and develop strong and peaceful institutions, and adaptation of Ukrainian legislation to EU law. Based on the analysis of existing approaches, the use of artificial intelligence for the development of tools that analyse large data sets of the Unified the State Register of Court Decisions to identify stable regularities in the judicial system’s functioning will be possible through the development of systems and assessment of risks to achieve the desired outcome of civil cases, increase the percentage of funds awarded, ensure the effective use of public funds for the maintenance of the judiciary in the state, and promote its growth equally across levels in society. The study showed that it is necessary to determine the indicators of the justice system’s efficient functioning and the main factors affecting their efficient consideration of civil cases by the court, as well as the reasons for the risk of excessive length in court proceedings, non-enforcement of court decisions, and high court costs. The findings show that it is necessary to develop a catalogue of information from the analysed cases by taking into account the legal proceedings’ administrative performance indicators in civil cases, as well as the identification of their main and significant factors affecting the effectiveness of legal proceedings and the derivation of quantitative and effective indicators.
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来源期刊
CiteScore
1.00
自引率
50.00%
发文量
62
审稿时长
6 weeks
期刊最新文献
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