Fairness Guarantees under Demographic Shift

This repository is the official reproduction of the implementation of Fairness Guarantees under Demographic Shift.

Requirements

Requires Python 3.x, Numpy 1.16+, and Cython 0.29+

To install further required packages and modules:

pip install -r requirements.txt

The pre-processed datasets, as well as the original datasets, are provided in the repository.

Training and Evaluation

The experiments from the paper can be executed by running the provided batch file from the Python directory, as follows:

./iclr_ds_experiments.bat

Once the experiments are completed, the figures can be generated by running the following batch file.

./iclr_ds_figures.bat

Given that the files are structured as follows:

- results
    - results_original_experiments
        - iclr_adult_{mode}_ds_rl_{constraint}
            - iclr_adult_{mode}_ds_rl_{constraint}.h5
        - iclr_brazil_{mode}_ds_rl_{constraint}
            - iclr_brazil_{mode}_ds_rl_{constraint}.h5
        - etc.
            - etc.
    - results_mlp_experiments
        - etc.
            - etc.
    - results_diabetes_experiments   
        - etc.

Where {mode} can consist of either 'fixed' or 'antag', corresponding to a known and unknown distributional shift respectively, and {constraint} can correspond to 'di' and 'dp', meaning the fairness constraints Disparate Impact and Demographic Parity.

Once completed, the figures will be saved to Fairness-Guarantees-under-Demographic-Shift/figures/* by default.

Experiment results

You can download results of our experiments here

Overall results

Our experiments support the following claims made in the original paper:

• Claim 1: High Confidence Fairness Guarantee

  Reproduction of the original experiments as well as the conducted additional experiments, show that this claims holds. Namely, Shifty never returns and unfair model. This is shown by utilizing an unseen dataset and a different classifier.

• Claim 2: Minor Loss of Accuracy

  The original and additional experiments show strong support for this claim, as results indeed show only a 3% loss in accuracy when comparing Shifty to the other baseline fairness algorithms.

• Claim 3: Finding a Solution

  In this study not enough evidence was found to support this claim, namely that Shifty avoids returning NO_SOLUTION_FOUND when increasing the number of samples in the training data.

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Contributing

SeldonianML is released under the MIT license.