RULER: Discriminative and Iterative Adversarial Training for Deep Neural Network Fairness

1.Ruler

​ Our technique will mitigate unfairness during the process of training, which leads to a more concise deployment.

1.1.Train

	Come into the `Ruler` directory, and run below commands. You will obtain the result shown in our paper.

Adult(census)

python -u main.py --dataset adult --adv_ratio 0.3 --adv_epoch 70 --accuracy_threshold 83.5 --protected_attribs 0 6 7 --gpu_id 0 --save_path results_adult

Bank

python -u main.py --dataset bank --adv_ratio 0.3 --adv_epochs 70 --accuracy_threshold 89.5 --protected_attribs 0   --save_path result_bank  

German

python -u main.py --dataset german --adv_ratio 0.3 --protected_attribs 6 9 --accuracy_threshold 77.0 --save_path results_german --lr 0.003

COMPAS

python -u main.py --dataset compas --accuracy_threshold 75.5 --adv_ratio 0.3 --protected_attribs 2 --save_path results_compas --adv_epochs 70

1.2.Metrics

​ Unfairness and accuracy, we take compas dataset as an instance.

​ Attention. The parameters model_start and model_end depends on the your own choice.

​ The parameters sample_round and num_gen determine the test scale. You can take (sample_round=10, num_gen=100) for a sample test.

python -u evaluators/evaluate_main.py --dataset compas --mode unfairness --model_start XX --model_end XX --model_path results_compas/train  --sample_round 100 --num_gen 10000

python -u evaluators/evaluate_main.py --dataset compas --mode test-acc --model_start XX --model_end XX --model_path results_compas/train