---
OA_place: publisher
OA_type: hybrid
_id: '19848'
abstract:
- lang: eng
  text: 'Binding precedents (súmulas vinculantes) constitute a juridical instrument
    unique to the Brazilian legal system and whose objectives include the protection
    of the Federal Supreme Court against repetitive demands. Studies of the effectiveness
    of these instruments in decreasing the Court’s exposure to similar cases, however,
    indicate that they tend to fail in such a direction, with some of the binding
    precedents seemingly creating new demands. We empirically assess the legal impact
    of five binding precedents, 11, 14, 17, 26, and 37, at the highest Court level
    through their effects on the legal subjects they address. This analysis is only
    possible through the comparison of the Court’s ruling about the precedents’ themes
    before they are created, which means that these decisions should be detected through
    techniques of Similar Case Retrieval, which we tackle from the angle of Case Classification.
    The contributions of this article are therefore twofold: on the mathematical side,
    we compare the use of different methods of Natural Language Processing — TF-IDF,
    LSTM, Longformer, and regex — for Case Classification, whereas on the legal side,
    we contrast the inefficiency of these binding precedents with a set of hypotheses
    that may justify their repeated usage. We observe that the TF-IDF models performed
    slightly better than LSTM and Longformer when compared through common metrics;
    however, the deep learning models were able to detect certain important legal
    events that TF-IDF missed. On the legal side, we argue that the reasons for binding
    precedents to fail in responding to repetitive demand are heterogeneous and case-dependent,
    making it impossible to single out a specific cause. We identify five main hypotheses,
    which are found in different combinations in each of the precedents studied.'
acknowledgement: Open access funding provided by Institute of Science and Technology
  (IST Austria).
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Raphaël
  full_name: Tinarrage, Raphaël
  id: 40ebcc9d-905f-11ef-bf0a-dc475da8a04e
  last_name: Tinarrage
  orcid: 0000-0002-1404-1095
- first_name: Henrique
  full_name: Ennes, Henrique
  last_name: Ennes
- first_name: Lucas
  full_name: Resck, Lucas
  last_name: Resck
- first_name: Lucas T.
  full_name: Gomes, Lucas T.
  last_name: Gomes
- first_name: Jean R.
  full_name: Ponciano, Jean R.
  last_name: Ponciano
- first_name: Jorge
  full_name: Poco, Jorge
  last_name: Poco
citation:
  ama: Tinarrage R, Ennes H, Resck L, Gomes LT, Ponciano JR, Poco J. Empirical analysis
    of binding precedent efficiency in Brazilian Supreme Court via case classification.
    <i>Artificial Intelligence and Law</i>. 2025. doi:<a href="https://doi.org/10.1007/s10506-025-09458-6">10.1007/s10506-025-09458-6</a>
  apa: Tinarrage, R., Ennes, H., Resck, L., Gomes, L. T., Ponciano, J. R., &#38; Poco,
    J. (2025). Empirical analysis of binding precedent efficiency in Brazilian Supreme
    Court via case classification. <i>Artificial Intelligence and Law</i>. Springer
    Nature. <a href="https://doi.org/10.1007/s10506-025-09458-6">https://doi.org/10.1007/s10506-025-09458-6</a>
  chicago: Tinarrage, Raphaël, Henrique Ennes, Lucas Resck, Lucas T. Gomes, Jean R.
    Ponciano, and Jorge Poco. “Empirical Analysis of Binding Precedent Efficiency
    in Brazilian Supreme Court via Case Classification.” <i>Artificial Intelligence
    and Law</i>. Springer Nature, 2025. <a href="https://doi.org/10.1007/s10506-025-09458-6">https://doi.org/10.1007/s10506-025-09458-6</a>.
  ieee: R. Tinarrage, H. Ennes, L. Resck, L. T. Gomes, J. R. Ponciano, and J. Poco,
    “Empirical analysis of binding precedent efficiency in Brazilian Supreme Court
    via case classification,” <i>Artificial Intelligence and Law</i>. Springer Nature,
    2025.
  ista: Tinarrage R, Ennes H, Resck L, Gomes LT, Ponciano JR, Poco J. 2025. Empirical
    analysis of binding precedent efficiency in Brazilian Supreme Court via case classification.
    Artificial Intelligence and Law.
  mla: Tinarrage, Raphaël, et al. “Empirical Analysis of Binding Precedent Efficiency
    in Brazilian Supreme Court via Case Classification.” <i>Artificial Intelligence
    and Law</i>, Springer Nature, 2025, doi:<a href="https://doi.org/10.1007/s10506-025-09458-6">10.1007/s10506-025-09458-6</a>.
  short: R. Tinarrage, H. Ennes, L. Resck, L.T. Gomes, J.R. Ponciano, J. Poco, Artificial
    Intelligence and Law (2025).
corr_author: '1'
date_created: 2025-06-15T22:01:31Z
date_published: 2025-05-26T00:00:00Z
date_updated: 2026-06-18T08:34:38Z
day: '26'
ddc:
- '510'
department:
- _id: UlWa
doi: 10.1007/s10506-025-09458-6
external_id:
  arxiv:
  - '2407.07004'
  isi:
  - '001494836700001'
fulldoi: https://doi.org/10.1007/s10506-025-09458-6
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1007/s10506-025-09458-6
month: '05'
oa: 1
oa_version: Published Version
publication: Artificial Intelligence and Law
publication_identifier:
  eissn:
  - 1572-8382
  issn:
  - 0924-8463
publication_status: epub_ahead
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Empirical analysis of binding precedent efficiency in Brazilian Supreme Court
  via case classification
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2025'
...