ARTIFICIAL INTELLIGENCE IN BRAZILIAN COURTS: A SCIENTIFIC ANALYSIS OF THE IMPLEMENTATION, PROJECTS, AND IMPACTS OF THE DIGITAL TRANSFORMATION OF THE JUDICIARY (2019–2026) (REMUNOM, 2026)
The study analyzes the implementation of artificial intelligence within the Brazilian Judiciary from 2019 to 2026, examining its institutional foundations, principal projects, operational impacts, and regulatory challenges. It begins with the milestone established by CNJ Ordinance No. 25/2019, which formally institutionalized Inova PJe, and extends to the regulatory consolidation introduced by CNJ Resolution No. 615/2025, positioning AI as a structural axis of the Judiciary’s digital transformation. The general objective is to evaluate, based on scientific evidence and institutional data, how AI solutions have been implemented, what efficiency-related outcomes they have produced, and which ethical and regulatory implications have emerged throughout this process. Regarding the specific objectives, the study develops: (i) a review of the literature on judicial backlog and technological innovation; (ii) a comparative analysis of the ten principal AI projects currently in operation (Victor, JuLIA, Hannah, STJ Logos, LexIA, ELIS, Projeto Larry, HORUS/ÁMON/TOTH, Sócrates/Athos, and Assistente TJMG), highlighting distinct functional typologies such as automated triage, appellate admissibility review, tax enforcement automation, comparative jurisprudence analysis, and generative AI applications; (iii) an examination of the empirical results of the Justice 4.0 Program, the SINAPSES Platform, and the nationwide expansion of AI initiatives; and (iv) a discussion of risks related to algorithmic bias, hallucinations in generative systems, data governance, and regulatory compliance. The methodology adopts a mixed-method research design, combining a structured review of the scientific literature, documentary analysis of regulatory acts and institutional reports, and secondary analysis of official quantitative data, organized into four analytical dimensions: operational efficiency, technological architecture, governance, and ethical-regulatory risks. The findings indicate growth to 140 AI projects distributed across 62 judicial bodies, with reductions of up to 95% in processing time in specific cases and increases of up to 67% in productivity in tax enforcement proceedings. The study concludes that AI has generated substantial gains in institutional efficiency; however, it also underscores the need for continuous strengthening of auditing mechanisms, technical capacity-building, and regulatory refinement to ensure equity, transparency, and democratic legitimacy.