FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software
<|reference_start|>FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software: Data-driven software solutions have significantly been used in critical domains with significant socio-economic, legal, and ethical implications. The rapid adoptions of data-driven solutions, however, pose major threats to the trustworthiness of automated decision-support software. A diminished understanding of the solution by the developer and historical/current biases in the data sets are primary challenges.   To aid data-driven software developers and end-users, we present \toolname, a debugging tool to test and explain the fairness implications of data-driven solutions. \toolname visualizes the logic of datasets, trained models, and decisions for a given data point. In addition, it trains various models with varying fairness-accuracy trade-offs. Crucially, \toolname incorporates counterfactual fairness testing that finds bugs beyond the development datasets. We conducted two studies through \toolname that allowed us to measure false positives/negatives in prevalent counterfactual testing and understand the human perception of counterfactual test cases in a class survey. \toolname and its benchmarks are publicly available at~\url{https://github.com/Pennswood/FairLay-ML}. The live version of the tool is available at~\url{https://fairlayml-v2.streamlit.app/}. We provide a video demo of the tool at https://youtu.be/wNI9UWkywVU?t=127<|reference_end|>
@article{yu2024fairlay-ml:,
    title={FairLay-ML: Intuitive Debugging of Fairness in Data-Driven
  Social-Critical Software},
    author={Normen Yu and Luciana Carreon and Gang Tan and Saeid Tizpaz-Niari},
    journal={arXiv preprint arXiv:2407.01423},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01423},
    primaryClass={cs.SE cs.CY cs.LG}
}