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Process mining-enabled jurimetrics: analysis of a Brazilian court's judicial performance in the business law processing

Authors:

Adriana Jacoto Unger,

José Francisco dos Santos Neto,

Marcelo Fantinato,

Sarajane Marques Peres,

Julio Trecenti,

Renata HirotaAuthors Info & Claims

ICAIL '21: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law

Pages 240 - 244

https://doi.org/10.1145/3462757.3466137

Published: 27 July 2021 Publication History

Abstract

Improving judicial performance has become increasingly relevant to guarantee access to justice for all, worldwide. In this context, technology-enabled tools to support lawsuit processing emerge as powerful allies to enhance the justice efficiency. Using electronic lawsuit management systems within the courts of justice is a widespread practice, which also leverages production of big data within judicial operation. Some jurimetrics techniques have arisen to evaluate efficiency based on statistical analysis and data mining of data produced by judicial information systems. In this sense, the process mining area offers an innovative approach to analyze judicial data from a process-oriented perspective. This paper presents the application of process mining in a event log derived from a dataset containing business lawsuits from the Court of Justice of the State of Sao Paulo, Brazil - the largest court in the world - in order to analyze judicial performance. Although the results show these lawsuits have an ad hoc sequence flow, process mining analysis have allowed to identify most frequent activities and process bottlenecks, providing insights into the root causes of inefficiencies.

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Cited By

Nai RSulis EMeo RGorgerino FRacca GGenga L(2025)Process Mining on a Public Procurement Dataset: A Case StudyMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74630-7_35(477-492)Online publication date: 8-Feb-2025
https://doi.org/10.1007/978-3-031-74630-7_35
Campi ACeri SDilettis MPernici B(2025)Variants Analysis in Judicial Trials: Challenges and Initial ResultsMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74630-7_30(425-438)Online publication date: 8-Feb-2025
https://doi.org/10.1007/978-3-031-74630-7_30
Bianchini DBono CCampi ACappiello CCeri SDe Luzi FMecella MPernici BPlebani P(2024)Challenges in AI-supported Process Analysis in the Italian Judicial System: what After Digitalization?Digital Government: Research and Practice10.1145/36300255:1(1-10)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1145/3630025
Show More Cited By

Index Terms

Process mining-enabled jurimetrics: analysis of a Brazilian court's judicial performance in the business law processing
1. Applied computing
  1. Enterprise computing
    1. Business process management
  2. Law, social and behavioral sciences
    1. Law
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Business intelligence
  2. Information systems applications
    1. Data mining

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cover image ACM Conferences

ICAIL '21: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law

June 2021

319 pages

ISBN:9781450385268

DOI:10.1145/3462757

Conference Chair:
Juliano Maranhão
University of São Paulo, Brazil
,
Program Chair:
Adam Zachary Wyner
Swansea University, United Kingdom

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2021

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ICAIL '21

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SIGAI

ICAIL '21: Eighteenth International Conference for Artificial Intelligence and Law

June 21 - 25, 2021

São Paulo, Brazil

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Overall Acceptance Rate 69 of 169 submissions, 41%

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Cited By

Nai RSulis EMeo RGorgerino FRacca GGenga L(2025)Process Mining on a Public Procurement Dataset: A Case StudyMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74630-7_35(477-492)Online publication date: 8-Feb-2025
https://doi.org/10.1007/978-3-031-74630-7_35
Campi ACeri SDilettis MPernici B(2025)Variants Analysis in Judicial Trials: Challenges and Initial ResultsMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74630-7_30(425-438)Online publication date: 8-Feb-2025
https://doi.org/10.1007/978-3-031-74630-7_30
Bianchini DBono CCampi ACappiello CCeri SDe Luzi FMecella MPernici BPlebani P(2024)Challenges in AI-supported Process Analysis in the Italian Judicial System: what After Digitalization?Digital Government: Research and Practice10.1145/36300255:1(1-10)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1145/3630025
Racis SSpano ALatti G(2024)Process reengineering and public value creation: using process mining in courtsInternational Journal of Public Sector Management10.1108/IJPSM-02-2024-0049Online publication date: 9-Dec-2024
https://doi.org/10.1108/IJPSM-02-2024-0049
Vercosa LSilva VCruz JBastos-Filho CBezerra B(2024)Investigation of lawsuit process duration using machine learning and process miningDiscover Analytics10.1007/s44257-024-00015-02:1Online publication date: 15-Jul-2024
https://doi.org/10.1007/s44257-024-00015-0
De Sousa Araújo TFerreira Lima ROliveira AD'Castro RGusmão BPaulo RDe França Guerra J(2023)Investigating the Usability and Comprehensibility of Process Mining Tools Within an Application-Specific Context2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10393888(4337-4342)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10393888
Vercosa LBastos-Filho CBezerra B(2023)An Approach for Analysing Law Processes based on Hierarchical Activities and Clustering2023 IEEE Latin American Conference on Computational Intelligence (LA-CCI)10.1109/LA-CCI58595.2023.10409389(1-6)Online publication date: 29-Oct-2023
https://doi.org/10.1109/LA-CCI58595.2023.10409389
Li CShinde THe WLau SHiew MTam NJoshi Avan der Aalst W(2023)Unveiling Bottlenecks in Logistics: A Case Study on Process Mining for Root Cause Identification and Diagnostics in an Air Cargo TerminalService-Oriented Computing10.1007/978-3-031-48424-7_21(291-307)Online publication date: 20-Nov-2023
https://doi.org/10.1007/978-3-031-48424-7_21
Piest JBemthuis RCutinha JArachchige JBukhsh F(2023)A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining TechniquesE-Business and Telecommunications10.1007/978-3-031-36840-0_7(118-136)Online publication date: 22-Jul-2023
https://doi.org/10.1007/978-3-031-36840-0_7

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