AI as the Court: Assessing AI Deployment in Civil Cases

Themeli, Erlis; Philipsen, Stefan

doi:10.2139/ssrn.3791553

SSRN Journal

2021

DOI: 10.2139/ssrn.3791553

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AI as the Court: Assessing AI Deployment in Civil Cases

Erlis Themeli

Stefan Philipsen²

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Cited by 2 publications

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“…Besides, judges should focus on different key information while dealing with various cases and this requires them to have a wealth of priori knowledge of different types of cases, which would take them a certain amount of time to learn and understand knowledge in unfamiliar areas (Guthrie et al, 2007). Moreover, manually searching or matching depends heavily on the carefulness and experience of the judges, which are of great uncertainty and instability (Liu, 2023;Themeli & Philipsen, 2021). If there is an approach to automatically extract key events from the materials of cases accurately, both labor and time costs would be reduced.…”

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Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Zhang,

Li,

Sheng

et al. 2024

Expert Systems

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In the formal procedure of Chinese civil cases, the textual materials provided by different parties describe the development process of the cases. It is a difficult but necessary task to extract the key information for the cases from these textual materials and to clarify the dispute focus of related parties. Currently, officers read the materials manually and use methods, such as keyword searching and regular matching, to get the target information. These approaches are time‐consuming and heavily depend on prior knowledge and the carefulness of the officers. To assist the officers in enhancing working efficiency and accuracy, we conduct a case study of detecting disputes from Chinese divorce cases based on proposing a Two‐Round‐Labeling (TRL) event extracting technique in this article. We implement the Judicial Intelligent Assistant (JIA) system according to the proposed approach to (1) automatically extract focus events from divorce case materials, (2) align events by identifying co‐reference among them, and (3) detect conflicts among events brought by the plaintiff and the defendant. With the JIA system, it is convenient for judges to determine the disputed issues in Chinese divorce cases. Experimental results demonstrate that the proposed approach and system can obtain the focus of Chinese divorce cases and detect conflicts more effectively and efficiently compared with the existing method.

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confidence: 99%

Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Zhang,

Li,

Sheng

et al. 2024

Expert Systems

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Perceptions of Justice By Algorithms

et al. 2022

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Artificial Intelligence and algorithms are increasingly able to replace human workers in cognitively sophisticated tasks, including ones related to justice. Many governments and international organizations are discussing policies related to the application of algorithmic judges in courts. In this paper, we investigate the public perceptions of algorithmic judges. Across two experiments (N = 1,822), and an internal meta-analysis (N = 3,039), our results show that even though court users acknowledge several advantages of algorithms (i.e., cost and speed), they trust human judges more and have greater intentions to go to the court when a human (vs. an algorithmic) judge adjudicates. Additionally, we demonstrate that the extent that individuals trust algorithmic and human judges depends on the nature of the case: trust for algorithmic judges is especially low when legal cases involve emotional complexities (vs. technically complex or uncomplicated cases).

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AI as the Court: Assessing AI Deployment in Civil Cases

Cited by 2 publications

References 14 publications

Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Perceptions of Justice By Algorithms

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