Testing Fairness in Human Decisions With Machine Learning Algorithmic Bias

Data (included in the data/ folder)

• Adult, Bank, and Heart datasets
  • Raw data comes from UCI Machine Learning Repository.
• Compas dataset
  • Raw data comes from propublica

Function

• inject_exp_all() function in the src/main.py file runs all experiments with injected bias.
• one_exp() function in the src/exp.py file has the main pipeline for each experiment.
• inject_bias() function in the src/exp.py file injects bias to the training data.
• FairBalance() function in the src/preprocessor.py file balances the class distribution within each demographic group.
• Metrics class in the src/metrics.py file calculates the metrics of accuracy, f1 score, EOD, and AOD.

Usage

0. Install dependencies:

pip install -r requirements.txt

1. Navigate to the source code:

cd src

2. Generate results in inject_results/

python main.py inject_exp_all