A survey of recent methods for addressing AI fairness and bias in
biomedicine

A survey of recent methods for addressing AI fairness and bias in biomedicine

13 February 2024

Furong Huang

Papers citing "A survey of recent methods for addressing AI fairness and bias in biomedicine"

6 / 6 papers shown

Title
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing Jitao Wang C. Shi John D. Piette Joshua R. Loftus Donglin Zeng Zhenke Wu OffRL 58 0 0 10 Jan 2025
Demystifying Large Language Models for Medicine: A Primer Qiao Jin Nicholas Wan Robert Leaman Shubo Tian Zhizheng Wang ... Chunhua Weng Ronald M. Summers Qingyu Chen Yifan Peng Zhiyong Lu LM&MA 32 3 0 24 Oct 2024
Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare Yifan Yang Qiao Jin Qingqing Zhu Zhizheng Wang Francisco Erramuspe Álvarez Nicholas Wan Benjamin Hou Zhiyong Lu LM&MA ELM 37 0 0 24 Oct 2024
Mitigating Health Disparities in EHR via Deconfounder Zheng Liu Xiaohan Li Philip Yu CML 41 8 0 28 Oct 2022
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary Jean-Francois Rajotte S. Mukherjee Caleb Robinson Anthony Ortiz Christopher West J. L. Ferres R. Ng FedML MedIm 122 40 0 18 Jan 2021
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,203 0 23 Aug 2019