Selecting and ranking leading cases in Brazilian Supreme Court decisions

Author(s)

De Souza, Jackson Jose ; Finger, Marcelo ; de Araujo, Jorge Alberto A. ; Maranhao, Juliano

• Document type: Journal article
• Source: KNOWLEDGE ENGINEERING REVIEW; v. 38, p. 18-pg., 2023-06-14.

Abstract

This work studies quantitative measures for ranking judicial decisions by the Brazilian Supreme Court [Supremo Tribunal Federal (STF)] and selecting leading cases, which are understood as those with broadness of influence on different legal fields. The measures are based on a network built over decisions whose cases were finalized in the Brazilian Supreme Court between 01/2001 and 12/2019, which were obtained by crawling publicly available STF records. Three ranking measures are proposed; two are adaptations of the PageRank algorithm, and one adapts Kleinberg's algorithm. Such measures are compared with respect to agreement on top 100 rankings; we also analyze each robustness measure based on self-agreement under perturbation.We examine whether the resulting quantitative ranking is congenial to a qualitative intuition of what the legal community usually considers as relevant precedents. We also discuss some possible criteria of relevance in the seek for patterns that suggest how quantitative and qualitative measures would better align. The ranking of leading cases and relevant decisions improved after building decision networks without irrelevant appeals and decisions that overflow the court offers a starting point to discuss the role of STF in the Brazilian judicial system.In our last work, both versions of PageRank and Kleinberg algorithms produced different rankings and all of them were robust with respect to 10% and 20%-perturbation levels, but none of them retrieved leading cases at the top of these rankings. Then, we took a further step in the studies of the STF decision network and we introduced better filtering of network nodes guided by legal expertise on the works of the Supreme Court. We also introduced more fine-grained perturbance levels to understand the impact of such filters in the STF decision network. We concluded that after filtering low-relevance decision types, the STF decision network is still robust under 10%-perturbation, but it presents higher degradation by increasing perturbation levels. The two versions of PageRank still produce different rankings. Kleinberg's algorithm provides a different ranking, with many relevant criminal cases. Although we improved algorithms rankings filtering decisions from the network, which represents an important methodological step, there is still room for improvement. Given that relevant decisions are well ranked after filtering out a large amount of irrelevant decisions, the results set a starting point to discuss the role of STF in the Brazilian judicial system. (AU)

• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 20/06443-5 - Spira: system for early-detection of respiratory insufficiency by voice audio analysis
• Grantee: Marcelo Finger
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program