<ul><li>Recent supervised learning methods aimed at capturing aleatoric and epistemic uncertainty may overlook model bias.</li><li>A more detailed categorization of epistemic uncertainty sources reveals that current methods do not fully encompass all aspects of epistemic uncertainty.</li><li>Simulation-based assessments demonstrate that existing methods often underestimate epistemic uncertainty due to model bias, leading to inaccurate estimates.</li><li>Proper representation of all sources of epistemic uncertainty is critical for accurate aleatoric estimates in machine learning models.</li></ul>

Why Machine Learning Models Fail to Fully Capture Epistemic Uncertainty

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