by Brittany Johnson and Yuriy Brun
Abstract:
Advances in how we build and use software, specifically the integration of machine learning for decision making, have led to widespread concern around model and software fairness. We present fairkit-learn, an interactive Python toolkit designed to support data scientists' ability to reason about and understand model fairness. We outline how fairkit-learn can support model training, evaluation, and comparison and describe the potential benefit that comes with using fairkit-learn in comparison to the state-of-the-art. Fairkit-learn is open source at https://go.gmu.edu/fairkit-learn/.
Citation:
Brittany Johnson and Yuriy Brun, Fairkit-learn: A Fairness Evaluation and Comparison Toolkit, in Proceedings of the Demonstrations Track at the 44th International Conference on Software Engineering (ICSE), 2022, pp. 70–74.
Bibtex Entry:
@inproceedings{Johnson22,
author = {Brittany Johnson and Yuriy Brun},
title =
{Fairkit-learn: {A}
Fairness Evaluation and Comparison Toolkit},
booktitle = {Proceedings of the Demonstrations Track at the 44th
International Conference on Software Engineering (ICSE)},
venue = {ICSE Demo},
address = {Pittsburgh, PA, USA},
month = {May},
date = {22--27},
year = {2022},
pages = {70--74},
doi = {10.1145/3510454.3516830},
note = {DOI: 10.1145/3510454.3516830},
accept = {$\frac{49}{98} = 50\%$},
abstract = {Advances in how we build and use software, specifically the integration of
machine learning for decision making, have led to widespread concern around
model and software fairness. We present fairkit-learn, an interactive Python
toolkit designed to support data scientists' ability to reason about and
understand model fairness. We outline how fairkit-learn can support model
training, evaluation, and comparison and describe the potential benefit that
comes with using fairkit-learn in comparison to the state-of-the-art.
Fairkit-learn is open source at https://go.gmu.edu/fairkit-learn/.},
fundedBy = {NSF CCF-1763423, Google, Meta Platforms, and Kosa.ai},
}