Aequitas Flow: Streamlining Fair ML Experimentation
<|reference_start|>Aequitas Flow: Streamlining Fair ML Experimentation: Aequitas Flow is an open-source framework for end-to-end Fair Machine Learning (ML) experimentation in Python. This package fills the existing integration gaps in other Fair ML packages of complete and accessible experimentation. It provides a pipeline for fairness-aware model training, hyperparameter optimization, and evaluation, enabling rapid and simple experiments and result analysis. Aimed at ML practitioners and researchers, the framework offers implementations of methods, datasets, metrics, and standard interfaces for these components to improve extensibility. By facilitating the development of fair ML practices, Aequitas Flow seeks to enhance the adoption of these concepts in AI technologies.<|reference_end|>
@article{jesus2024aequitas,
    title={Aequitas Flow: Streamlining Fair ML Experimentation},
    author={S'ergio Jesus, Pedro Saleiro, In^es Oliveira e Silva, Beatriz M.
  Jorge, Rita P. Ribeiro, Jo~ao Gama, Pedro Bizarro, Rayid Ghani},
    journal={arXiv preprint arXiv:2405.05809},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05809},
    primaryClass={cs.LG cs.AI cs.CY}
}