AI Technologies in the Judiciary: Experiences, Innovations, and Possibilities
DOI:
https://doi.org/10.5753/compbr.2026.56.8611Keywords:
Artificial Intelligence (AI), Digital Transformation, Judicial EfficiencyAbstract
The Brazilian justice system faces the challenge of handling one of the largest caseloads in the world, which imposes significant demands to ensure timeliness and quality in judicial decisions. With the advancement of Artificial Intelligence (AI), especially with the introduction of large language models and advanced data analysis techniques, the digital transformation of the justice system has reached a new level. AI has been applied not only as a tool for task automation, but also as a strategic support to promote greater efficiency, speed, and accuracy in judicial activities. The technological advancement of the Judiciary, grounded in the use of AI and automation, provides systems capable of supporting judges and court staff in performing repetitive tasks, such as case classification, identification of precedents, document analysis, and organization of procedural information. This scenario enables a reduction in the time spent on operational activities, allowing judicial professionals to focus on tasks that require greater analytical and decision-making capabilities. Furthermore, this transformation also redefines the way the justice system interacts with society, promoting greater accessibility and inclusion through more intuitive and multichannel digital services. This article aims to present real-world successful experiences of AI-based systems developed at the Federal Court of Rio Grande do Norte (JFRN) in partnership with the Federal University of Rio Grande do Norte (UFRN). The paper highlights the innovations brought by these solutions, as well as presents possibilities for expanding the use of such technologies in the judicial context.
Downloads
References
BARROS, Márcia Maria Nunes de. O centro nacional de inteligência da justiça federal e a inteligência artificial: novas possibilidades. In: Estratégias de prevenção de conflitos, monitoramento e gestão de demandas e precedentes. Brasília: Centro de Estudos Judiciários, 2018, p. 84-87.
MENEZES-NETO, Elias Jacob de; BEZERRA, Fabio Luiz de Oliveira; CLEMENTINO, Marco Bruno Miranda. Evaluating LLMs for Healthcare-Related Named Entity Recognition in Brazilian Judicial Decisions. In: 2025 IEEE Symposium on Computers and Communications (ISCC). jul. 2025.
MENEZES-NETO, Elias Jacob de; CLEMENTINO, Marco Bruno Miranda. Using deep learning to predict outcomes of legal appeals better than human experts: A study with data from Brazilian federal courts. PLOS ONE, v. 17, n. 7, p. e0272287, 28 jul. 2022.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.