Academic paper on interpretable risk scoring systems designed to maximize decision net benefit. Focuses on building ML models that balance predictive performance with human interpretability for real-world decision-making.
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Learning An Interpretable Risk Scoring System for Maximizing Decision Net Benefit
Researchers develop interpretable ML risk scoring models that maintain decision-making quality while remaining explainable to domain experts—addressing a key tension in high-stakes applications like healthcare and criminal justice.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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