charannatra/brazilian_court_decisions

Dataset Card for predicting-brazilian-court-decisions

Dataset Summary

The dataset is a collection of 4043 Ementa (summary) court decisions and their metadata from the Tribunal de Justiça de Alagoas (TJAL, the State Supreme Court of Alagoas (Brazil). The court decisions are labeled according to 7 categories and whether the decisions were unanimous on the part of the judges or not. The dataset supports the task of Legal Judgment Prediction.

Supported Tasks and Leaderboards

Legal Judgment Prediction

Languages

Brazilian Portuguese

Dataset Structure

Data Instances

The file format is jsonl and three data splits are present (train, validation and test) for each configuration.

Data Fields

The dataset contains the following fields:

• process_number: A number assigned to the decision by the court
• orgao_julgador: Judging Body: one of '1ª Câmara Cível', '2ª Câmara Cível', '3ª Câmara Cível', 'Câmara Criminal', ' Tribunal Pleno', 'Seção Especializada Cível'
• publish_date: The date, when the decision has been published (14/12/2018 - 03/04/2019). At that time (in 2018-2019), the scraping script was limited and not configurable to get data based on date range. Therefore, only the data from the last months has been scraped.
• judge_relator: Judicial panel
• ementa_text: Summary of the court decision
• decision_description: Suggested input. Corresponds to ementa_text - judgment_text - unanimity_text. Basic statistics (number of words): mean: 119, median: 88, min: 12, max: 1400
• judgment_text: The text used for determining the judgment label
• judgment_label: Primary suggested label. Labels that can be used to train a model for judgment prediction:
  • no: The appeal was denied
  • partial: For partially favourable decisions
  • yes: For fully favourable decisions
  • removed labels (present in the original dataset):
    • conflito-competencia: Meta-decision. For example, a decision just to tell that Court A should rule this case and not Court B.
    • not-cognized: The appeal was not accepted to be judged by the court
    • prejudicada: The case could not be judged for any impediment such as the appealer died or gave up on the case for instance.
• unanimity_text: Portuguese text to describe whether the decision was unanimous or not.
• unanimity_label: Secondary suggested label. Unified labels to describe whether the decision was unanimous or not (in some cases contains not_determined); they can be used for model training as well (Lage-Freitas et al., 2019).

Data Splits

The data has been split randomly into 80% train (3234), 10% validation (404), 10% test (405).

There are two tasks possible for this dataset.

Judgment

Label Distribution

judgment	train	validation	test
no	1960	221	234
partial	677	96	93
yes	597	87	78
total	3234	404	405

Unanimity

In this configuration, all cases that have not_determined as unanimity_label can be removed.

Label Distribution

unanimity_label	train	validation	test
not_determined	1519	193	201
unanimity	1681	205	200
not-unanimity	34	6	4
total	3234	404	405

Dataset Creation

Curation Rationale

This dataset was created to further the research on developing models for predicting Brazilian court decisions that are also able to predict whether the decision will be unanimous.

Source Data

The data was scraped from Tribunal de Justiça de Alagoas (TJAL, the State Supreme Court of Alagoas (Brazil).

Initial Data Collection and Normalization

“We developed a Web scraper for collecting data from Brazilian courts. The scraper first searched for the URL that contains the list of court cases […]. Then, the scraper extracted from these HTML files the specific case URLs and downloaded their data […]. Next, it extracted the metadata and the contents of legal cases and stored them in a CSV file format […].” (Lage-Freitas et al., 2022)

Who are the source language producers?

The source language producer are presumably attorneys, judges, and other legal professionals.

Annotations

Annotation process

The dataset was not annotated.

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

The court decisions might contain sensitive information about individuals.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that, differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to have a look at the conversion script convert_to_hf_dataset.py in order to retrace the steps for converting the original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.

Additional Information

Lage-Freitas, A., Allende-Cid, H., Santana Jr, O., & Oliveira-Lage, L. (2019). Predicting Brazilian court decisions:

• "In Brazil [...] lower court judges decisions might be appealed to Brazilian courts (Tribiunais de Justiça) to be reviewed by second instance court judges. In an appellate court, judges decide together upon a case and their decisions are compiled in Agreement reports named Acóordãos."

Dataset Curators

The names of the original dataset curators and creators can be found in references given below, in the section Citation Information. Additional changes were made by Joel Niklaus (Email ; Github) and Veton Matoshi (Email ; Github).

Licensing Information

No licensing information was provided for this dataset. However, please make sure that you use the dataset according to Brazilian law.

Citation Information

@misc{https://doi.org/10.48550/arxiv.1905.10348,
  author = {Lage-Freitas, Andr{\'{e}} and Allende-Cid, H{\'{e}}ctor and Santana, Orivaldo and de Oliveira-Lage, L{\'{i}}via},
  doi = {10.48550/ARXIV.1905.10348},
  keywords = {Computation and Language (cs.CL),FOS: Computer and information sciences,Social and Information Networks (cs.SI)},
  publisher = {arXiv},
  title = {{Predicting Brazilian court decisions}},
  url = {https://arxiv.org/abs/1905.10348},
  year = {2019}
}

@article{Lage-Freitas2022,
  author = {Lage-Freitas, Andr{\'{e}} and Allende-Cid, H{\'{e}}ctor and Santana, Orivaldo and Oliveira-Lage, L{\'{i}}via},
  doi = {10.7717/peerj-cs.904},
  issn = {2376-5992},
  journal = {PeerJ. Computer science},
  keywords = {Artificial intelligence,Jurimetrics,Law,Legal,Legal NLP,Legal informatics,Legal outcome forecast,Litigation prediction,Machine learning,NLP,Portuguese,Predictive algorithms,judgement prediction},
  language = {eng},
  month = {mar},
  pages = {e904--e904},
  publisher = {PeerJ Inc.},
  title = {{Predicting Brazilian Court Decisions}},
  url = {https://pubmed.ncbi.nlm.nih.gov/35494851 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044329/},
  volume = {8},
  year = {2022}
}

Contributions

Thanks to @kapllan and @joelniklaus for adding this dataset.