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Found 4 papers, 3 papers with code
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness.
Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist
The widespread use of ChatGPT and other emerging technology powered by generative artificial intelligence (GenAI) has drawn much attention to potential ethical issues, especially in high-stakes applications such as healthcare, but ethical discussions are yet to translate into operationalisable solutions.
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes
This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes.
Shapley variable importance clouds for interpretable machine learning
Interpretable machine learning has been focusing on explaining final models that optimize performance.
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