Could the Decisions of Quasi-Judicial Institutions be Predicted by Machine Learning Techniques?

This study investigates the extent to which the conclusion of a decision can be predicted from other parts of the decision from quasi-judicial institutions using machine learning. Predicting conclusions in quasi-judicial bodies poses unique challenges and opportunities because the case pool is smaller and less diversified than that of judicial bodies. The European Committee of Social Rights (ECSR), one of the quasi-judicial organizations, was chosen as the research's focus point to address this difficulty. All ECSR decisions on collective complaints are used as data and analysed using four distinct machine-learning methods. Despite the limited data from fewer rulings by quasi-judicial entities such as the ECSR, the analysis correctly predicts their conclusions with reasonable accuracy. It is suggested that applications for collective complaints can be made more effective, efficient, and successful by picking the model with the highest prediction accuracy using machine learning techniques. [ABSTRACT FROM AUTHOR]