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RYEL System: A Novel Method for Capturing and Represent Knowledge in a Legal Domain Using Explainable Artificial Intelligence (XAI) and Granular Computing (GrC)

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Interpretable Artificial Intelligence: A Perspective of Granular Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 937))

Abstract

The need for studies connecting the machine’s explainability with granularity is very important, especially for a detailed understanding of how data is fragmented and processed according to the domain of discourse. We develop a system called RYEL based on subject-matter experts about the legal case process, facts, pieces of evidence, and how to analyze the merits of a case. Through this system, we study the Explainable Artificial Intelligence (XAI) approach using Knowledge Graphs (KG) and enforcement unsupervised algorithms which results are expressed in an Explanatory Graphical Interface (EGI). The evidence and facts of a legal case are represented as knowledge graphs. Granular Computing (GrC) techniques are applied in the graph when processing nodes and edges using object types, properties, and relations. Through RYEL we propose new definitions for Explainable Artificial Intelligence (XAI) and Interpretable Artificial Intelligence (IAI) in a much better way and will help us to cover a technological spectrum that has not yet been covered and promises to be a new area of study which we call Interpretation-Assessment/Assessment-Interpretation (IA-AI) that consists not only in explaining machine inferences but the interpretation and assessment from a user according to a context. It is proposed a new focus-centered organization in which the XAI-IAI will be able to work and will allow us to explain in more detail the method implemented by RYEL. We believe our system has an explanatory and interpretive nature and could be used in other domains of discourse, some examples are: (1) the interpretation a doctor has about a disease and the assessment of using certain medicine, (2) the interpretation a psychologist has from a patient and the assessment for a psychological application treatment, (3) or how a mathematician interprets a real-world problem and makes an assessment about which mathematical formula to use. However, now we focus on the legal domain.

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Acknowledgements

BISITE Research Group and the Faculty of Law, both from the University of Salamanca (USAL); the School of Computer Science and Informatics (ECCI) and the Postgraduate Studies System (SEP), both from the University of Costa Rica (UCR); the Edgar Cervantes Villalta School of the Judicial Branch of Costa Rica; special thanks to all the judges and AI experts who have participated in this investigation from Mexico, Costa Rica, Spain, Panama, and Argentina.

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Authors and Affiliations

  1. Universidad de Costa Rica, San José, Costa Rica

    Luis Raúl Rodríguez Oconitrillo, Juan José Vargas, Arturo Camacho & Alvaro Burgos

  2. Universidad de Salamanca, Bisite Research Group, Salamanca, Spain

    Juan Manuel Corchado

  3. Air Institute, IoT Digital Innovation Hub, Salamanca, Spain

    Juan Manuel Corchado

  4. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka, Japan

    Juan Manuel Corchado

  5. Pusat Komputeran dan Informatik, Universiti Malaysia Kelantan, Kelantan, Malaysia

    Juan Manuel Corchado

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  1. Luis Raúl Rodríguez Oconitrillo
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  2. Juan José Vargas
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  3. Arturo Camacho
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  4. Alvaro Burgos
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Correspondence to Luis Raúl Rodríguez Oconitrillo .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering Edmonton, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Oconitrillo, L.R.R., Vargas, J.J., Camacho, A., Burgos, A., Corchado, J.M. (2021). RYEL System: A Novel Method for Capturing and Represent Knowledge in a Legal Domain Using Explainable Artificial Intelligence (XAI) and Granular Computing (GrC). In: Pedrycz, W., Chen, SM. (eds) Interpretable Artificial Intelligence: A Perspective of Granular Computing. Studies in Computational Intelligence, vol 937. Springer, Cham. https://doi.org/10.1007/978-3-030-64949-4_12

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