We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four
Results for different basic classifiers (mean±SD) by using varied numbers of supplementary training data, trained and tested in 10-fold cross-validation on the plant dataset.