@inproceedings{Poulain2023Improve,
author = {Poulain, Raphael and Bin Tarek, Mirza Farhan and Beheshti, Rahmatollah},
    AUTHOR+an = {1=student; 2=student; 3=lead},
    addendum = {Acceptance rate: 25\%},
title = {Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods},
year = {2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3593013.3594102},
doi = {10.1145/3593013.3594102},
booktitle = {Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency},
pages = {1599–1608},
numpages = {10},
keywords = {Adversarial Fairness, Algorithmic Fairness, Federated Learning},
location = {Chicago, IL, USA},
series = {FAccT '23}
}

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