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Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare.
Agnostic Federated Learning
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Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
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A Survey on Bias and Fairness in Machine Learning
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Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
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Dipole: Diagnosis Prediction in Healthcare via Attention-Based Bidirectional Recurrent Neural Networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD ’17) . Association for Computing Machinery, New York, NY, USA, 1903–1911
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Minimax Group Fairness: Algorithms and Experiments
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Mitigating Bias in Federated Learning
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FairBatch: Batch Selection for Model Fairness
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Health, United States, 2015: With Special Feature on Racial and Ethnic Health Disparities
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MIMIC-III, a freely accessible critical care database
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Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics . PMLR, 1273–1282
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Practical Secure Aggregation for Privacy-Preserving Machine Learning. In CCS
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Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record
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Why is My Classifier Discriminatory?. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (Montréal, Canada) (NIPS’18) . Curran Associates Inc., Red Hook, NY, USA, 3543–3554
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Adversarial Removal of Demographic Attributes from Text Data. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Brussels, Belgium, 11–21
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The eICU Collaborative Research Database, a freely available multi-center database for critical care research
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InfoFair: Information-Theoretic Intersectional Fairness
Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, and Hanghang Tong. 2021 · 2021
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Ditto: Fair and Robust Federated Learning Through Personalization. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 6357–6368
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Modeling Techniques for Machine Learning Fairness: A Survey
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Federated Learning with Fair Averaging
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Improving Fairness via Federated Learning
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Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
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Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 120–129
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Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, Greg Ver Steeg, and Aram Galstyan. 2019 · 2019
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Preserving patient privacy while training a predictive model of in-hospital mortality
Pulkit Sharma, Farah E Shamout, and David A Clifton. 2019 · 2019
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Fairness in Machine Learning for Healthcare
Muhammad Aurangzeb Ahmad, Arpit Patel, Carly Eckert, Vikas Kumar, and Ankur Teredesai. 2020 · 2020
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Flower: A Friendly Federated Learning Research Framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D Lane. 2020 · 2020
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Security and privacy of electronic health records: Concerns and challenges
Ismail Keshta and Ammar Odeh. 2021 · 2020
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On the Convergence of FedAvg on Non-IID Data. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2020 · 2020
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Racial and Ethnic Diversity in the United States: 2010 Census and 2020 Census
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Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
Trenton Chang, Michael W. Sjoding, and Jenna Wiens. 2022 · 2022
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Federated Learning for Electronic Health Records
Trung Kien Dang, Xiang Lan, Jianshu Weng, and Mengling Feng. 2022 · 2022
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Fair Machine Learning in Healthcare: A Review
Qizhang Feng, Mengnan Du, Na Zou, and Xia Hu. 2022 · 2022
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Fair Federated Learning via Bounded Group Loss
Shengyuan Hu, Zhiwei Steven Wu, and Virginia Smith. 2022 · 2022
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Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
Ričards Marcinkevičs, Ece Ozkan, and Julia E. Vogt. 2022 · 2022
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Federated learning for smart healthcare: A survey
Dinh C Nguyen, Quoc-Viet Pham, Pubudu N Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia Dobre, and Won-Joo Hwang. 2022 · 2022
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Minimax Demographic Group Fairness in Federated Learning
Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro, and Miguel Rodrigues. 2022 · 2022
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Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records
Raphael Poulain, Mehak Gupta, and Rahmatollah Beheshti. 2022 · 2022
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Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model
Eliane Röösli, Selen Bozkurt, and Tina Hernandez-Boussard. 2022 · 2022
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