detoxai 0.3.4

todo

DetoxAI is a Python package for debiasing neural networks. It provides a simple and efficient way to remove bias from your models while maintaining their performance. The package is designed to be easy to use and integrate into existing projects. We hosted a website with a demo and an overview of the package, which can be found at https://detoxai.github.io.

Installation

DetoxAI is available on PyPI, and can be installed by running the following command:

 pip install detoxai

Quickstart

The snippet below shows the high-level API of DetoxAI and how to use it.

import detoxai

model = ...
dataloader = ... # has to output a tuple of three tensors: (x, y, protected attributes)

corrected = detoxai.debias(model, dataloader)

metrics = corrected["SAVANIAFT"].get_all_metrics() # Get metrics for the model debiased with SavaniAFT method
model = corrected["SAVANIAFT"].get_model()

A shortest snippet that would actually run and shows how to plug DetoxAI into your code is below.

import torch
import torchvision
import detoxai

model = torchvision.models.resnet18(pretrained=True)
model.fc = torch.nn.Linear(model.fc.in_features, 2)  # Make it binary classification

X = torch.rand(128, 3, 224, 224)
Y = torch.randint(0, 2, size=(128,))
PA = torch.randint(0, 2, size=(128,))

dataloader = torch.utils.data.DataLoader(list(zip(X, Y, PA)), batch_size=32)

results: dict[str, detoxai.CorrectionResult] = detoxai.debias(model, dataloader)

Too see more examples of detoxai in use, navigate to the github repo https://github.com/DetoxAI/detoxai and see examples/ folder.

Acknowledgment

If you use this library in your work please cite as:

@misc{detoxai2025,
  author={Ignacy Stepka and Lukasz Sztukiewicz and Michal Wilinski and Jerzy Stefanowski},
  title={{DetoxAI}: a {Python} Package for Debiasing Neural Networks},
    year={2025},
  url={https://github.com/DetoxAI/detoxai},
}

License

MIT License