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Legal judgment prediction aims to automatically forecast the outcome of court cases based on textual descriptions of case facts. This study addresses this gap by evaluating a range of neural models on an English legal judgment prediction dataset sourced from the European Court of Human Rights. The investigation establishes robust baselines that outperform previous feature-based models across three key tasks: binary violation classification, multi-label classification, and case importance prediction. Additionally, the study examines potential biases in the models with respect to demographic information through data anonymization techniques, highlighting concerns about fairness and impartiality in predictive legal systems.
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