An adversarial training framework for mitigating algorithmic biases in clinical machine learning
<p>Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating...
Main Authors: | Yang, J, Soltan, AAS, Eyre, DW, Yang, Y, Clifton, DA |
---|---|
Format: | Journal article |
Jezik: | English |
Izdano: |
Springer Nature
2023
|
Podobne knjige/članki
-
An adversarial training framework for mitigating algorithmic biases in clinical machine learning
od: Jenny Yang, et al.
Izdano: (2023-03-01) -
Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
od: Yang, J, et al.
Izdano: (2023) -
Mitigating machine learning bias between high income and low–middle income countries for enhanced model fairness and generalizability
od: Yang, J, et al.
Izdano: (2024) -
Privacy-aware early detection of COVID-19 through adversarial training
od: Rohanian, M, et al.
Izdano: (2022) -
Deep reinforcement learning for multi-class imbalanced training: applications in healthcare
od: Yang, J, et al.
Izdano: (2023)