metadata

annotations_creators:
  - expert-generated
language:
  - de
  - fr
  - it
  - en
language_creators:
  - expert-generated
  - found
license:
  - cc-by-sa-4.0
multilinguality:
  - multilingual
pretty_name: OcclusionSwissJudgmentPrediction
size_categories:
  - 1K<n<10K
source_datasets:
  - extended|swiss_judgment_prediction
tags:
  - explainability-judgment-prediction
  - occlusion
task_categories:
  - text-classification
  - other
task_ids: []

Dataset Card for "OcclusionSwissJudgmentPrediction": An implementation of an occlusion based explainability method for Swiss judgment prediction

Table of Contents
Dataset Summary
Dataset structure
Dataset Creation
Additional Information

Dataset Summary

This dataset contains an implementation of occlusion for the SwissJudgmentPrediction task.

Documents

Occlusion-Swiss-Judgment-Prediction is a subset of the Swiss-Judgment-Prediction dataset. The Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Occlusion-Swiss-Judgment-Prediction extends this dataset by adding sentence splitting with explainability labels.

Supported Tasks and Leaderboards

OcclusionSwissJudgmentPrediction can be used for performing the occlusion in the legal judgment prediction task.

Languages

Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.

Dataset structure

Data Instances

Multilingual use of the dataset

When the dataset is used in a multilingual setting selecting the the 'all_languages' flag:

from datasets import load_dataset
dataset = load_dataset('occlusion_swiss_judgment_prediction', 'all_languages')```


For occlusion:

```json
{
  "id": 57542,
  "year": 2017,
  "label": "dismissal",
  "language": "de",
  "region": "Espace_Mittelland",
  "canton": "BE",
  "legal_area": "social_law",
  "explainability_label": "Supports judgment",
  "occluded_text": "Mit Entscheid vom 4. Mai 2017 hiess das Verwaltungsgericht des Kantons Bern die Beschwerde teilweise gut, hob die angefochtene Verf\u00fcgung auf und wies die Sache an die IV-Stelle zur\u00fcck, damit sie, nach Vornahme der Abkl\u00e4rungen im Sinne der Erw\u00e4gungen, \u00fcber den Rentenanspruch neu verf\u00fcge",
  "text": "Sachverhalt: A. Die 1987 geborene, aus Bolivien stammende A.A._ leidet an einer pr\u00e4natalen Retinopathie und einer dadurch bedingten schweren Sehbehinderung beidseits. Im Dezember 2003 reiste sie in die Schweiz ein. Im Mai 2007 verheiratete sie sich mit B.A._. Am 22. Juni 2007 meldete sie sich bei der Invalidenversicherung zum Leistungsbezug an. Am 6. Februar 2008 lehnte die IV-Stelle Bern das Leistungsgesuch verf\u00fcgungsweise ab, weil die versicherungsm\u00e4ssigen Voraussetzungen nicht erf\u00fcllt seien. Am 22. Oktober 2009 stellte die Versicherte ein neues Leistungsgesuch, auf welches die IV-Stelle mit Verf\u00fcgung vom 16. Dezember 2009 nicht e**int**rat. Am 27. Juli 2010 meldete sich A.A._ erneut bei der Invalidenversicherung zum Leistungsbezug an, wobei sie eine Verschlechterung der Sehkraft geltend machte. Mit Verf\u00fcgung vom 10. November 2010 sprach ihr die IV-Stelle ab 1. August 2010 eine Entsch\u00e4digung f\u00fcr leichte Hilflosigkeit zu. Soweit das Gesuch den Invalidenrentenanspruch betraf, trat die IV-Stelle darauf nicht ein (Verf\u00fcgung vom 19. November 2010). Im Juli 2011 erteilte die IV-Stelle Kostengutsprache f\u00fcr berufliche Eingliederungsmassnahmen, welche die Versicherte vorzeitig abbrach. Mit Verf\u00fcgung vom 27. Januar 2016 er\u00f6ffnete die IV-Stelle der Versicherten, dass sie keinen Anspruch auf eine Invalidenrente habe. Der Versicherungsfall sei bereits vor der Einreise in die Schweiz eingetreten. B. A.A._ liess Beschwerde einreichen mit dem Rechtsbegehren, unter Aufhebung der Verf\u00fcgung vom 27. Januar 2016 sei ihr eine ausserordentliche Invalidenrente zuzusprechen.  . C. Die IV-Stelle f\u00fchrt Beschwerde in \u00f6ffentlich-rechtlichen Angelegenheiten mit dem Antrag, der vorinstanzliche Entscheid sei aufzuheben. "
}

Monolingual use of the dataset

When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:

from datasets import load_dataset
dataset = load_dataset('occlusion_swiss_judgment_prediction', 'de_1')

For occlusion:

{
  "id": 57542,
  "year": 2017,
  "label": "dismissal",
  "language": "de",
  "region": "Espace_Mittelland",
  "canton": "BE",
  "legal_area": "social_law",
  "explainability_label": "Supports judgment",
  "occluded_text": "Mit Entscheid vom 4. Mai 2017 hiess das Verwaltungsgericht des Kantons Bern die Beschwerde teilweise gut, hob die angefochtene Verf\u00fcgung auf und wies die Sache an die IV-Stelle zur\u00fcck, damit sie, nach Vornahme der Abkl\u00e4rungen im Sinne der Erw\u00e4gungen, \u00fcber den Rentenanspruch neu verf\u00fcge",
  "text": "Sachverhalt: A. Die 1987 geborene, aus Bolivien stammende A.A._ leidet an einer pr\u00e4natalen Retinopathie und einer dadurch bedingten schweren Sehbehinderung beidseits. Im Dezember 2003 reiste sie in die Schweiz ein. Im Mai 2007 verheiratete sie sich mit B.A._. Am 22. Juni 2007 meldete sie sich bei der Invalidenversicherung zum Leistungsbezug an. Am 6. Februar 2008 lehnte die IV-Stelle Bern das Leistungsgesuch verf\u00fcgungsweise ab, weil die versicherungsm\u00e4ssigen Voraussetzungen nicht erf\u00fcllt seien. Am 22. Oktober 2009 stellte die Versicherte ein neues Leistungsgesuch, auf welches die IV-Stelle mit Verf\u00fcgung vom 16. Dezember 2009 nicht e**int**rat. Am 27. Juli 2010 meldete sich A.A._ erneut bei der Invalidenversicherung zum Leistungsbezug an, wobei sie eine Verschlechterung der Sehkraft geltend machte. Mit Verf\u00fcgung vom 10. November 2010 sprach ihr die IV-Stelle ab 1. August 2010 eine Entsch\u00e4digung f\u00fcr leichte Hilflosigkeit zu. Soweit das Gesuch den Invalidenrentenanspruch betraf, trat die IV-Stelle darauf nicht ein (Verf\u00fcgung vom 19. November 2010). Im Juli 2011 erteilte die IV-Stelle Kostengutsprache f\u00fcr berufliche Eingliederungsmassnahmen, welche die Versicherte vorzeitig abbrach. Mit Verf\u00fcgung vom 27. Januar 2016 er\u00f6ffnete die IV-Stelle der Versicherten, dass sie keinen Anspruch auf eine Invalidenrente habe. Der Versicherungsfall sei bereits vor der Einreise in die Schweiz eingetreten. B. A.A._ liess Beschwerde einreichen mit dem Rechtsbegehren, unter Aufhebung der Verf\u00fcgung vom 27. Januar 2016 sei ihr eine ausserordentliche Invalidenrente zuzusprechen.  . C. Die IV-Stelle f\u00fchrt Beschwerde in \u00f6ffentlich-rechtlichen Angelegenheiten mit dem Antrag, der vorinstanzliche Entscheid sei aufzuheben. "
}

Data Fields

The following data fields are provided for documents (train, validation):

id: (int) a unique identifier of the for the document year: (int) the publication year text: (str) the facts of the case label: (class label) the judgment outcome: 0 (dismissal) or 1 (approval) language: (str) one of (de, fr, it) region: (str) the region of the lower court canton: (str) the canton of the lower court legal area: (str) the legal area of the case

The following data fields are provided for documents (occ_test):

id: (int) a unique identifier of the for the document year: (int) the publication year label: (str) the judgment outcome: dismissal or approval language: (str) one of (de, fr, it) region: (str) the region of the lower court canton: (str) the canton of the lower court legal area: (str) the legal area of the case explainability_label (str): the explainability label assigned to the occluded text: Supports judgment, Opposes judgment, Neutral, Baseline occluded_text (str): the occluded text text: (str) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)

Note that Baseline cases are only contained in version 1 of the occlusion test set, since they do not change from experiment to experiment.

Data Splits

Language | Subset | Number of Documents (Training/Validation/Test) German| de_1| 35'452 / 4'705 / 427 German| de_2| 35'452 / 4'705 / 1366 German| de_3| 35'452 / 4'705 / 3567 German| de_4| 35'452 / 4'705 / 7235 French | fr_1 | 21'179 / 3'095 / 307 French | fr_2 | 21'179 / 3'095 / 854 French | fr_3 | 21'179 / 3'095 / 1926 French | fr_4 | 21'179 / 3'095 / 3279 Italian | it_1| 3'072 / 408 / 299 Italian | it_2| 3'072 / 408 / 919 Italian | it_3| 3'072 / 408 / 2493 Italian | it_4| 3'072 / 408 / 5733 All | all | 59'709 / 8'208 / 28375

Dataset Creation

Curation Rationale

The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.

Source Data

Initial Data Collection and Normalization

The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.

Who are the source language producers?

Annotations

Annotation process

The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts, splitting sentences/group of sentences and annotated with one of the following explainability label: Supports judgment, Opposes Judgment and Neutral. The test sets have each sentence/ group of sentence once occluded, enabling an analysis of the changes in the model's performance. The legal expert annotation were conducted from April 2020 to August 2020.

Who are the annotators?

Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).

Personal and Sensitive Information

The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.

Additional Information

Dataset Curators

Niklaus et al. (2021) and Nina Baumgartner

Licensing Information

We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)

The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.

Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf

Citation Information

@misc{baumgartner_nina_occlusion_2019,
    title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland},
    shorttitle = {From Occlusion to Transparancy},
    abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.},
    author = {{Baumgartner, Nina}},
    year = {2022},
    langid = {english}
    }

Contributions

Thanks to @ninabaumgartner for adding this dataset.