Business intelligence model to support a judge's decision making about legal situations

Oconitrillo, Luis Raúl Rodríguez; Osegueda, Álvaro de la Ossa

doi:10.1109/concapan.2016.7942362

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…Te comprehensive assessment by Mandal et al [29] stood out as a seminal work that spanned over ffty-six unique combinations of document representation techniques and similarity measures, validating the efcacy of conventional vectorization techniques while indicating areas for improvement. Authors' appraisal [45] Manual KE Facts Te authors did not describe the similarity assessment method in detail No evaluation [46] AI wave Facts, time, references…”

Section: Rq3: What Is the Taxonomy Of Existing Methods Andmentioning

confidence: 99%

“…Later, a business intelligence (BI) solution was proposed by Oconitrillo and De La Ossa Osegueda [45] to support judges' decision making. Te authors advocated a new formal representation of legal cases, depending on facts and attributes.…”

Section: Content Analysismentioning

confidence: 99%

“…Tese were followed by research that expanded the geographical scope to include criminal summary judgments from Taiwan [53] and Islamic legislation [51]. Tere were also more focused datasets, such as those involving the Law of Negligence [50] and judicial declarations of abandonment in Costa Rican Juvenile Courts [45].…”

Section: Data Sourcesmentioning

confidence: 99%

See 2 more Smart Citations

Automation of Legal Precedents Retrieval: Findings from a Literature Review

Mentzingen,

António,

Bacao

2023

International Journal of Intelligent Systems

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Judges frequently rely their reasoning on precedents. Courts must preserve uniformity in decisions while, depending on the legal system, previous cases compel rulings. The search for methods to accurately identify similar previous cases is not new and has been a vital input, for example, to case-based reasoning (CBR) methodologies. This literature review offers a comprehensive analysis of the advancements in automating the identification of legal precedents, primarily focusing on the paradigm shift from manual knowledge engineering to the incorporation of Artificial Intelligence (AI) technologies such as natural language processing (NLP) and machine learning (ML). While multiple approaches harnessing NLP and ML show promise, none has emerged as definitively superior, and further validation through statistically significant samples and expert-provided ground truth is imperative. Additionally, this review employs text-mining techniques to streamline the survey process, providing an accurate and holistic view of the current research landscape. By delineating extant research gaps and suggesting avenues for future exploration, this review serves as both a summation and a call for more targeted, empirical investigations.

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Section: Rq3: What Is the Taxonomy Of Existing Methods Andmentioning

confidence: 99%

Section: Content Analysismentioning

confidence: 99%

See 1 more Smart Citation

Automation of Legal Precedents Retrieval: Findings from a Literature Review

Mentzingen,

António,

Bacao

2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

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Section: Data Sourcesmentioning

confidence: 99%

Automation of legal precedents retrieval: findings from a literature review

Mentzingen,

Bacao,

António

2023

Preprint

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Judges frequently rely their reasoning on precedents. Courts must preserve uniformity in decisions while, depending on the legal system, previous cases compel rulings. The search for methods to accurately identify similar previous cases is not new and has been a vital input, for example, to case-based reasoning (CBR) methodologies. This literature review offers a comprehensive analysis of the advancements in automating the identification of legal precedents, primarily focusing on the paradigm shift from Manual Knowledge Engineering to the incorporation of Artificial Intelligence (AI) technologies such as Natural Language Processing (NLP) and Machine Learning (ML). While multiple approaches harnessing NLP and ML show promise, none has emerged as definitively superior, and further validation through statistically significant samples and expert-provided ground truth is imperative. Additionally, this review employs text-mining techniques to streamline the survey process, providing an accurate and holistic view of the current research landscape. By delineating extant research gaps and suggesting avenues for future exploration, this review serves as both a summation and a call for more targeted, empirical investigations.

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“…Thus, this research aims not only to investigate the behavioral development of the judge when using new technology for in-depth analysis of cases but also to show computational advances with a high impact in cognitive and psychological fields. So, this research presents a Mixture of Experts (MOE) system [22,23] called RYEL [24][25][26][27]. This system was created based on CBR guidelines [3,[28][29][30] and Explainable Artificial Intelligence (XAI) [31][32][33] using focus-centered organization fundamentals, which means the organization of XAI and CBR is done and focus according to the perspective and approach that a human has in a domain of discourse, meaning it is human-centric [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

RYEL: An Experimental Study in the Behavioral Response of Judges Using a Novel Technique for Acquiring Higher-Order Thinking Based on Explainable Artificial Intelligence and Case-Based Reasoning

et al. 2021

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The need for studies connecting machine explainability with human behavior is essential, especially for a detailed understanding of a human’s perspective, thoughts, and sensations according to a context. A novel system called RYEL was developed based on Subject-Matter Experts (SME) to investigate new techniques for acquiring higher-order thinking, the perception, the use of new computational explanatory techniques, support decision-making, and the judge’s cognition and behavior. Thus, a new spectrum is covered and promises to be a new area of study called Interpretation-Assessment/Assessment-Interpretation (IA-AI), consisting of explaining machine inferences and the interpretation and assessment from a human. It allows expressing a semantic, ontological, and hermeneutical meaning related to the psyche of a human (judge). The system has an interpretative and explanatory nature, and in the future, could be used in other domains of discourse. More than 33 experts in Law and Artificial Intelligence validated the functional design. More than 26 judges, most of them specializing in psychology and criminology from Colombia, Ecuador, Panama, Spain, Argentina, and Costa Rica, participated in the experiments. The results of the experimentation have been very positive. As a challenge, this research represents a paradigm shift in legal data processing.

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Business intelligence model to support a judge's decision making about legal situations

Cited by 4 publications

References 6 publications

Automation of Legal Precedents Retrieval: Findings from a Literature Review

Automation of Legal Precedents Retrieval: Findings from a Literature Review

Automation of legal precedents retrieval: findings from a literature review

RYEL: An Experimental Study in the Behavioral Response of Judges Using a Novel Technique for Acquiring Higher-Order Thinking Based on Explainable Artificial Intelligence and Case-Based Reasoning

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