For details about our experiments on the NODA dataset, please refer to here.
Reproduction can only be done using ProPublica (located in dataset/propublica) as NODA cannot be published for confidential reasons. Our code for data processing on NODA cannot be shared for the same reason.
This code has been tested on Ubuntu.
First, install Python 3.x, Numpy (1.16+), and Cython (0.29+).
The remaining dependencies can be installed by executing the following command from the Python directory of :
pip install -r requirements.txt
The experiments presented in the report can be executed by running the following line in the command:
python -m experiments.bandit.recidivism recidivism_all --n_trials 5 --definition GroupFairness --e 0.1 \
--d 0.05 --ci_type ttest --n_iters 2000 --n_jobs 15 --r_train_v_test 0.4 --r_cand_v_safe 0.4 --rwd_recid -1.0 \
--rwd_nonrecid 1.0 --use_score_text --data_pct 1.0 --add_info all
recidivism_all: folder name to save the results--add_info: covariate sets to use ('none', 'all', 'judge', 'screen_ada', 'trial_ada') - only applicable to NODA dataset
Model output comes in .h5 file format. reading robinhood output.ipynb in ipynb folder will transform the result to a well-formated dataframe. We provide recidivism_judge.h5 as an sample output file.
Original code comes from https://github.com/sgiguere/RobinHood-NeurIPS-2019, and it has been modified to be applicable for our experiments on NODA.
Code for RobinHood is released under the MIT license, with the exception of the code for FairMachineLearning (located in Python/baselines/fairml.py), which are released under their licences assigned by their respective authors.