Code for Accurate Fairness: Improving Individual Fairness without Trading Accuracy (AAAI-23) Siamese fairness in-processing for improving the accurate fairness of a machine learning model. For example: to improve the accurate fairness on the Ctrip dataset, First run prepare_ctrip_data.py to generate the augmentated training dataset, Then run train_ctrip_siamese_fairness.py to improve the accurate fairness. How to check whether the predications of a machine learning model are accurately fair? For example: to check the accurate fairness on the Ctrip dataset, First run prepare_ctrip_data.py to generate the test dataset, Then run get_ctrip_result.py to print out the accuracy, individual fairness, group fairness and accurate fairness measurements of the baseline model and the corresponding Siamese fairness model, in the following formats a table, similar to Table 3 in our main paper, for statistical results of each model with various sensitive attributes a chart X_Y.pdf in .\pic, similar to Figure 2 in our main paper, for fairness confusion matrix performances of dataset X under method Y (e.g., bl=baseline, sf=Siamese fairness) a chart X_Fairea.pdf in .\pic, similar to Figure 3 in our main paper, for Fairea evaluation of dataset X