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...
| Autores principales: | , , , , |
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| Formato: | Journal article |
| Lenguaje: | English |
| Publicado: |
Springer Nature
2023
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