Robust Ranking of Brazilian Supreme Court Decisions

Souza, Jackson José de; Finger, Marcelo

doi:10.1007/978-3-030-61377-8_40

Cited by 1 publication

(10 citation statements)

References 11 publications

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“…In our last work (de Souza & Finger, 2020) we showed the decision network is robust for 10% and 20% of perturbations against the network. However, we failed to find a substantial number of decisions that are leading cases or influential decisions whose legal thesis is reproduced in further decisions.…”

Section: Decision Network Modeling and Analysesmentioning

confidence: 91%

“…As data extraction and network modeling were already done in our previous work, we briefly describe those tasks here for the sake of reference. For a detailed description, please refer to de Souza and Finger (2020).…”

Section: Decision Network Modeling and Analysesmentioning

confidence: 99%

“…We applied three algorithms for ranking decisions in the decision network and evaluated its robustness as we did in de Souza and Finger (2020): , , and Kleinberg’s. We call in this work as algorithms the and models that are different equations to calculate the PageRank value of each decision using the PageRank algorithm (Brin & Page, 1998) and we call a measure the result of a ranking algorithm.…”

Section: Decision Network Modeling and Analysesmentioning

confidence: 99%

“…This work aims to be another step in the discussion of the desired properties of quantitative feasible measures of relevance of Supreme Court decisions. We believe our research is a relevant contribution to the literature because (1) the Supremo Tribunal Federal (STF) decision network has not been sufficiently explored in the literature to understand its structure and to find a good measure to rank leading cases; (2) we are applying a new measure of network robustness that we have proposed in de Souza and Finger (2020); and (3) we are also applying filters under the guidance of legal experts designed to consider specific aspects of STF functioning.…”

Section: Introductionmentioning

confidence: 99%

“…Based upon a such complex network and the concept of authority scores, we keep studying the decision ranking measures built on PageRank (Page et al ., 1999) and Kleinberg’s (1999) ranking algorithms proposed in de Souza and Finger (2020). Our goal is to investigate the level of agreement between measures , their robustness with respect to the network, and whether most of best-ranked decisions by the measures are leading cases .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Selecting and ranking leading cases in Brazilian Supreme Court decisions

Souza¹,

Finger²,

Araújo³

et al. 2023

The Knowledge Engineering Review

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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.

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Section: Decision Network Modeling and Analysesmentioning

confidence: 91%

Section: Decision Network Modeling and Analysesmentioning

confidence: 99%