Adversarial Learning for Bias Mitigation

This repository hosts the version of the code used for the publication "An adversarial training framework for mitigating algorithmic biases in clinical machine learning".

Dependencies

This will be updated soon.

Getting Started

adversarial_training_framework.py

This file contains the Adv_Model class and performance metrics.

trainer.py

This file takes a JSON configuration file as input and uses the Adv_Model class to train either a basic model or an adversarial model. An example of a JSON configuration file:

{
  "Xtrain": "data/X_train.pkl",
  "Xvalid": "data/X_valid.pkl",
  "ytrain": "data/y_train.pkl",
  "yvalid": "data/y_valid.pkl",
  "ztrain": "data/Z_ethnicity_train.pkl",
  "zvalid": "data/Z_ethnicity_valid.pkl",
  "method": "adv",
  "num_classes": 2,
  "hyperparams": {
    "learning_rate": [1e-3],
    "num_iters": [4000],
    "num_nodes": [30],
    "num_nodes_adv": [10],
    "dropout_rate": [0.3],
    "alpha": [1]
  }
}

To train a model, use:

run trainer.py "/path/to/JSONCONFIG"

Citation

If you found our work useful, please consider citing:

Yang, J., Soltan, A. A., Eyre, D. W., Yang, Y., & Clifton, D. A. (2023). An adversarial training framework for mitigating algorithmic biases in clinical machine learning. NPJ Digital Medicine, 6(1), 55.