Upload 4 files

# Lower Court Insertion for Swiss Judgment Prediction
This PR contains the 3 language test set to perform the lower court insertion in the swiss judgment prediction and the corresponding dataset card. This PR is done in agreement with Joel Niklaus.
Lower-Court-Insertion-Swiss-Judgment-Prediction extends Swiss-Judgment-Prediction by adding lower court insertion. The test sets should be used in combination with the Swiss-Judgment-Prediction training and validation sets. The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.
## Additional Information
### Data Splits
Language | Subset | Number of Documents (Training/Validation/Test)
German| de| 35'452 / 4'705 / 378
French | fr| 21'179 / 3'095 / 414
Italian | it| 3'072 / 408 / 335
All | all | 59'709 / 8'208 / 1127

Other additional information was added to the dataset card.

README_lower_court_insertion.md

@@ -0,0 +1,215 @@
+---
+annotations_creators:
+- expert-generated
+language:
+- de
+- fr
+- it
+- en
+language_creators:
+- expert-generated
+- found
+license:
+- cc-by-sa-4.0
+multilinguality:
+- multilingual
+pretty_name: LowerCourtInsertionSwissJudgmentPrediction
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|swiss_judgment_prediction
+tags:
+- explainability-judgment-prediction
+task_categories:
+- text-classification
+- other
+task_ids: []
+---
+
+# Dataset Card for "LowerCourtInsertionSwissJudgmentPrediction": An implementation of lower court insertion bias analysis for Swiss judgment prediction
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Summary](#dataset-summary)
+  - [Documents](#documents)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset **str**ucture](#dataset-**str**ucture)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Summary
+
+This dataset contains an implementation of lower-court-insertion for the SwissJudgmentPrediction task.
+
+### Documents
+Lower-Court-Insertion-Swiss-Judgment-Prediction is a subset of the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/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. Lower-Court-Insertion-Swiss-Judgment-Prediction extends this dataset by adding lower court insertion.
+### Supported Tasks and Leaderboards
+
+LowerCourtInsertionSwissJudgmentPrediction can be used for performing the LowerCourtInsertion 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:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset('lower_court_insertion_swiss_judgment_prediction', 'all_languages')```
+
+For lower_court-insertion:
+```json
+{
+  "id": 59810,
+  "year": 2017,
+  "label": "approval",
+  "language": "de",
+  "region": "Central_Switzerland",
+  "canton": "LU",
+  "legal_area": "social_law",
+  "explainability_label": "Lower court",
+  "text": "Sachverhalt: A. Die IV-Stelle Luzern sprach dem 1977 geborenen A._ im Anschluss an dessen Besuch einer Sonderschule berufliche Massnahmen und entsprechende Taggelder zu. Im Oktober 1997 (Postaufgabe) meldete sich der Versicherte erneut zum Bezug von Leistungen der Invalidenversicherung an. Im Feststellungsblatt vom 12. Oktober 1998 zum Rentenbeschluss vermerkte die IV-Stelle als Ausgangsbasis \"Jugendinvalidit\u00e4t\"; sodann errechnete sie einen Invalidit\u00e4tsgrad von 85,39 %. Mit Verf\u00fcgung vom 4. Dezember 1998 sprach sie A._ eine ganze Invalidenrente ab 1. Juli 1997 zu. Mit Verf\u00fcgung vom 7. Februar 2003, Mitteilung vom 15. Juni 2007 und Verf\u00fcgung vom 13. Mai 2008 wurde der bisherige Rentenanspruch jeweils (ohne n\u00e4here \u00dcberpr\u00fcfung der entsprechenden Voraussetzungen) best\u00e4tigt. Im Februar 2014 leitete die Verwaltung erneut ein Revisionsverfahren ein. Dabei veranlasste sie insbesondere das Gutachten des Dr. med. B._, Facharzt f\u00fcr Psychiatrie und Psychotherapie beim Regionalen \u00c4rztlichen Dienst (RAD), vom 13. November 2014 (\"neuropsychiatrisch-neuropsychologische Komplexfallabkl\u00e4rung\"). Am 10. Februar 2015 bot die IV-Stelle Luzern A._ Unterst\u00fctzung bei der Stellensuche an. Ab M\u00e4rz 2015 kam sie f\u00fcr zwei Arbeitsversuche w\u00e4hrend jeweils sechs Monaten auf. Zu einer anschliessenden Festanstellung kam es nicht. Nach Durchf\u00fchrung des Vorbescheidverfahrens hob die IV-Stelle mit Verf\u00fcgung vom 2. Juni 2016 die Invalidenrente wiedererw\u00e4gungsweise auf Ende Juli 2016 auf. B. Die dagegen erhobene Beschwerde hiess das Appellationsgericht Basel-Stadt mit Entscheid vom 3. Februar 2017 gut. Es hob die Verf\u00fcgung vom 2. Juni 2016 auf und bejahte einen weiterhin bestehenden Anspruch auf eine ganze Rente. C. Die IV-Stelle beantragt mit Beschwerde in \u00f6ffentlich-rechtlichen Angelegenheiten, der Entscheid vom 3. Februar 2017 sei aufzuheben, und ihre Verf\u00fcgung vom 2. Juni 2016 sei zu best\u00e4tigen. A._ schliesst auf Abweisung der Beschwerde. Das Bundesamt f\u00fcr Sozialversicherungen verzichtet auf eine Vernehmlassung. ",
+  "lower_court": "Appellationsgericht Basel-Stadt"
+}
+```
+
+#### 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:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset('lower-court-insertion_swiss_judgment_prediction', 'de')
+```
+
+For lower_court-insertion:
+```json
+{
+  "id": 59810,
+  "year": 2017,
+  "label": "approval",
+  "language": "de",
+  "region": "Central_Switzerland",
+  "canton": "LU",
+  "legal_area": "social_law",
+  "explainability_label": "Lower court",
+  "text": "Sachverhalt: A. Die IV-Stelle Luzern sprach dem 1977 geborenen A._ im Anschluss an dessen Besuch einer Sonderschule berufliche Massnahmen und entsprechende Taggelder zu. Im Oktober 1997 (Postaufgabe) meldete sich der Versicherte erneut zum Bezug von Leistungen der Invalidenversicherung an. Im Feststellungsblatt vom 12. Oktober 1998 zum Rentenbeschluss vermerkte die IV-Stelle als Ausgangsbasis \"Jugendinvalidit\u00e4t\"; sodann errechnete sie einen Invalidit\u00e4tsgrad von 85,39 %. Mit Verf\u00fcgung vom 4. Dezember 1998 sprach sie A._ eine ganze Invalidenrente ab 1. Juli 1997 zu. Mit Verf\u00fcgung vom 7. Februar 2003, Mitteilung vom 15. Juni 2007 und Verf\u00fcgung vom 13. Mai 2008 wurde der bisherige Rentenanspruch jeweils (ohne n\u00e4here \u00dcberpr\u00fcfung der entsprechenden Voraussetzungen) best\u00e4tigt. Im Februar 2014 leitete die Verwaltung erneut ein Revisionsverfahren ein. Dabei veranlasste sie insbesondere das Gutachten des Dr. med. B._, Facharzt f\u00fcr Psychiatrie und Psychotherapie beim Regionalen \u00c4rztlichen Dienst (RAD), vom 13. November 2014 (\"neuropsychiatrisch-neuropsychologische Komplexfallabkl\u00e4rung\"). Am 10. Februar 2015 bot die IV-Stelle Luzern A._ Unterst\u00fctzung bei der Stellensuche an. Ab M\u00e4rz 2015 kam sie f\u00fcr zwei Arbeitsversuche w\u00e4hrend jeweils sechs Monaten auf. Zu einer anschliessenden Festanstellung kam es nicht. Nach Durchf\u00fchrung des Vorbescheidverfahrens hob die IV-Stelle mit Verf\u00fcgung vom 2. Juni 2016 die Invalidenrente wiedererw\u00e4gungsweise auf Ende Juli 2016 auf. B. Die dagegen erhobene Beschwerde hiess das Appellationsgericht Basel-Stadt mit Entscheid vom 3. Februar 2017 gut. Es hob die Verf\u00fcgung vom 2. Juni 2016 auf und bejahte einen weiterhin bestehenden Anspruch auf eine ganze Rente. C. Die IV-Stelle beantragt mit Beschwerde in \u00f6ffentlich-rechtlichen Angelegenheiten, der Entscheid vom 3. Februar 2017 sei aufzuheben, und ihre Verf\u00fcgung vom 2. Juni 2016 sei zu best\u00e4tigen. A._ schliesst auf Abweisung der Beschwerde. Das Bundesamt f\u00fcr Sozialversicherungen verzichtet auf eine Vernehmlassung. ",
+  "lower_court": "Appellationsgericht Basel-Stadt"
+}
+```
+
+### 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 (lower_court_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: Lower court, Baseline
+text: (**str**) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)
+lower_court: (**str**) the inserted lower_court (for Baseline there is no insertion)
+
+### Data Splits
+Language |	Subset |	Number of Documents (Training/Validation/Test)
+German| 	de| 	35'452 / 4'705 / 378
+French |	fr|	21'179 / 3'095 / 414
+Italian |	it| 	3'072 / 408 / 335
+All |	all |	59'709 / 8'208 / 1127
+
+
+## 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?
+
+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.
+
+### Annotations
+
+#### Annotation process
+
+The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition the 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 with the lower court. These lower court annotations were then use the insert each lower court into each case once (instead of the original lower court). Allowing an analysis of the changes in the models performance for each inserted lower court, giving insight into a possible bias among them. 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)
+
+© Swiss Federal Supreme Court, 2000-2020
+
+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](https://github.com/ninabaumgartner) for adding this dataset.