An overview of information extraction techniques for legal document analysis and processing

Zadgaonkar, Ashwini V.; Agrawal, Avinash J.

doi:10.11591/ijece.v11i6.pp5450-5457

Cited by 13 publications

(6 citation statements)

References 37 publications

(33 reference statements)

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“…The globalised world today demands speedy and efficient handling of every action [1]- [3]. The fastmoving actions are essential in ensuring that the services can be implemented in line with the rapid development of technology and information, including in the legal system [4]- [20]. Judges and lawyers generally handle legal cases, but the help of technology is critically essential due to the massive numbers of cases daily.…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review of machine learning methods in predicting court decisions

Rosili

Hassan

Zakaria

et al. 2021

IJ-AI

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<span>Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.</span>

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Section: Introductionmentioning

confidence: 99%

A systematic literature review of machine learning methods in predicting court decisions

Rosili

Hassan

Zakaria

et al. 2021

IJ-AI

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“…11 AI has had a profound impact on electronic discovery (Mills and Uebergang 2017), introducing new advancements in various ways. Electronic discovery employs NLP to facilitate numerous functions related to the retrieval and extraction of information (Zadgaonkar and Agrawal 2021). This technology aids in the systematic analysis of extensive document collections by identifying pertinent terminology and proposing supplementary keywords that coincide with the designated search terms.…”

Section: Related Workmentioning

confidence: 99%

A support system for the detection of abusive clauses in B2C contracts

Dadas,

Kozłowski,

Poświata

et al. 2024

Artif Intell Law

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Many countries employ systemic methods of protecting consumers from unfair business practices. One such practice is the use of abusive clauses in business-to-consumer (B2C) contracts, which unfairly impose additional obligations on the consumer or deprive them of their due rights. This article presents an information system that utilizes artificial intelligence methods to automate contract analysis and to detect abusive clauses. The goal of the system is to support the entire administrative process, from contract acquisition, through text extraction and the recommendation of potentially abusive clauses, to the generation of official administrative documents that can be sent to court or to the owners of firms. This article focuses on on the components that use machine learning methods. The first is an intelligent crawler that is responsible for automatically detecting contract templates on websites and retrieving them into the system. The second is a document analysis module that implements a clause recommendation algorithm. The algorithm employs transformer-based language models and information retrieval methods to identify abusive passages in text. Our solution achieved first place in a competition on the automatic analysis of B2C contracts organized by the Polish Office of Competition and Consumer Protection (UOKiK), and has since been implemented as an official tool to support the contract analysis process in Poland.

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“…С помощью этой технологии можно выявить закономерности, сложные для человеческого восприятия; так, прогностическое моделирование помогает предсказать риск рецидивизма среди подсудимых и более эффективно распределять административные ресурсы (Henman, 2020). ИИ может извлекать информацию из объемных юридических документов, сокращая рутинную работу персонала (Zadgaonkar & Agrawal, 2021). В полиции эта технология используется для выявления районов с высоким уровнем преступности и распределения ресурсов на основе полученной информации (Perry, 2013).…”

Section: For Citation Of Russian Versionunclassified

Content Analysis of Judges’ Sentiments Toward Artificial Intelligence Risk Assessment Tools

Fine,

Le,

Miller

2024

RusJEL

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Objective: to analyze the positions of judges on risk assessment tools using artificial intelligence.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological.Results: Artificial intelligence (AI) uses computer programming to make predictions (e.g., bail decisions) and has the potential to benefit the justice system (e.g., save time and reduce bias). This secondary data analysis assessed 381 judges’ responses to the question, “Do you feel that artificial intelligence (using computer programs and algorithms) holds promise to remove bias from bail and sentencing decisions?”Scientific novelty: The authors created apriori themes based on the literature, which included judges’ algorithm aversion and appreciation, locus of control, procedural justice, and legitimacy. Results suggest that judges experience algorithm aversion, have significant concerns about bias being exacerbated by AI, and worry about being replaced by computers. Judges believe that AI has the potential to inform their decisions about bail and sentencing; however, it must be empirically tested and follow guidelines. Using the data gathered about judges’ sentiments toward AI, the authors discuss the integration of AI into the legal system and future research.Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering the issues related to the legal risks of using artificial intelligence.

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An overview of information extraction techniques for legal document analysis and processing

Cited by 13 publications

References 37 publications

A systematic literature review of machine learning methods in predicting court decisions

A systematic literature review of machine learning methods in predicting court decisions

A support system for the detection of abusive clauses in B2C contracts

Content Analysis of Judges’ Sentiments Toward Artificial Intelligence Risk Assessment Tools

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