The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Using machine learning to predict decisions of the European Court of Human Rights
Open accessAbstract
When courts started publishing judgements, big data analysis (i.e. large-scale statistical analysis of case law and machine learning) within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict (future) judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our (relatively simple) approach highlights the potential of machine learning approaches in the legal domain. We show, however, that predicting decisions for future cases based on the cases from the past negatively impacts performance (average accuracy range from 58 to 68%). Furthermore, we demonstrate that we can achieve a relatively high classification performance (average accuracy of 65%) when predicting outcomes based only on the surnames of the judges that try the case.
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LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
Chalkidis, I., Jana, A., Hartung, D., Bommarito, M. J., Androutsopoulos, I., Katz, D. M., & Aletras, N. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3936759 · Cited by 111
Porębski, A. (2023) · Research Handbook on Law and Technology · doi:10.4337/9781803921327.00037 · Cited by 4
Metadata
- Authors
- Medvedeva, M., Vols, M., & Wieling, M.
- Venue
- Artificial Intelligence and Law
- Published
- 2019-06-26
- Volume
- 28
- Issue
- 2
- Pages
- 237-266
- ISSN
- 0924-8463 (ISSN-L)
- 1572-8382
- OpenAlex
- W2954257417
- Cited by
- 429
- References
- 29
- FWCI
- 160.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
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