Using machine learning to predict decisions of the European Court of Human Rights
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 tr
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.
Executive Summary
This study examines the application of machine learning to predict decisions of the European Court of Human Rights (ECHR) using natural language processing tools. The authors develop a predictive model that achieves an average accuracy of 75% in predicting violations of nine articles of the European Convention on Human Rights. However, the model's performance is negatively impacted when predicting future decisions based on past cases, with accuracy ranging from 58 to 68%. The study also finds that predicting outcomes based on the surnames of the judges attempting the case yields a relatively high classification performance. These findings highlight the potential and limitations of machine learning approaches in the legal domain, with significant implications for the ECHR and other judicial bodies.
Key Points
- ▸ Machine learning approach achieves 75% accuracy in predicting ECHR decisions
- ▸ Predicting future decisions based on past cases negatively impacts performance
- ▸ Predicting outcomes based on judge surnames yields high classification performance
Merits
Methodological rigor
The study employs a robust methodology, utilizing natural language processing tools to analyze ECHR case law and machine learning algorithms to develop a predictive model.
Relevance to the legal domain
The study's findings have significant implications for the ECHR and other judicial bodies, highlighting the potential and limitations of machine learning approaches in the legal domain.
Demerits
Limited generalizability
The study's findings may not be generalizable to other jurisdictions or legal contexts, given the specificity of the ECHR and its case law.
Dependence on data quality
The accuracy of the predictive model may be sensitive to the quality and availability of data used to train the model.
Expert Commentary
While the study's findings are intriguing, they also raise important questions about the potential biases and limitations of machine learning approaches in the legal domain. For example, the study's reliance on past case law may perpetuate existing biases and inequalities in the ECHR's decision-making process. Furthermore, the study's focus on predicting outcomes based on judge surnames highlights the potential for implicit bias and stereotyping in judicial decision-making. As such, it is essential to approach the development and deployment of machine learning models in the legal domain with caution and a critical eye towards their potential limitations and biases.
Recommendations
- ✓ Future studies should investigate the development of more robust and transparent machine learning models that can mitigate biases and limitations in the ECHR's decision-making process.
- ✓ Judicial bodies and policymakers should engage in ongoing discussions about the potential applications and limitations of AI and machine learning in judicial decision-making and the rule of law.
