Research Center for Digital Sustainability

Explainability Annotations for Legal Judgment Prediction in Switzerland

Explainability Annotations for Legal Judgment Prediction in Switzerland

This project is available as a Bachelor's or Master's project.


Swiss court decisions are anonymized to protect the privacy of the involved people (parties, victims, etc.). Previous research [1] has shown that it is possible to re-identify companies involved in court decisions by linking the rulings with external data in certain cases. Our project tries to further build an automated system for re-identifying involved people from court rulings. This system can then be used as a test for the anonymization practice of Swiss courts. For more information regarding the overarching research project, please go here.

We recently presented a dataset for Legal Judgment Prediction (you try to predict the outcome of a case based on its facts) including 85K Swiss Federal Supreme Court decisions [2]. Although we achieved up to 80% Macro-F1 Score, the models still work as black boxes and are thus not interpretable. In this project, you will work together with a legal student, to annotate some decisions in the dataset for explainability. You will use the annotation tool Prodigy and write annotation guidelines for collecting high-quality annotations. 

Research Questions

So far, to the best of our knowledge, the Explainable AI methods have not been studied in Swiss Legal Judgment Prediction. 

RQ1: To what extent do current models predict the judgment outcome based on input deemed important by lawyers?


  1. Get familiar with the literature on explainable NLP. 
  2. Write the annotation guidelines.
  3. Set up Prodigy for the annotation process.
  4. Work together with the law student to collect the annotations. 
  5. Analyze the annotated data and run experiments based on the annotations.
  6. Describe the annotation process and the annotated data in detail.


⬤⬤⬤◯◯ Programming

⬤⬤⬤⬤◯ Experimentation

⬤◯◯◯◯ Literature


Good programming skills (preferably in Python)

Preferably experience in deep learning (transformers)


Joel Niklaus


[1] Vokinger, K.N., Mühlematter, U.J., 2019. Re-Identifikation von Gerichtsurteilen durch «Linkage» von Daten(banken). Jusletter 27.
[2] Niklaus, J et al. "Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark" Natural Legal Language Processing Workshop @ Empirical Methods for Natural Language Processing (2021)
[3] Malik, V., Sanjay, R., Nigam, S.K., Ghosh, K., Guha, S.K., Bhattacharya, A., & Modi, A. (2021). ILDC for CJPE: Indian Legal Documents Corpus for Court JudgmentPrediction and Explanation. ACL/IJCNLP.