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About this paper

Eyke Hüllermeier, Willem Waegeman

Eyke Hüllermeier and Willem Waegeman present a comprehensive overview distinguishing aleatoric and epistemic uncertainty in machine learning, surveying existing methods and formalisms for their representation and quantification. Their work provides a conceptual framework for developing more reliable AI systems capable of robustly handling inherent randomness and model-related ignorance.

Key insights

• The distinction between aleatoric and epistemic uncertainty is fundamental for guiding interventions, such as deciding whether to gather more data or improve the model architecture.
• Standard probability theory often struggles to adequately represent a pure "lack of knowledge" (epistemic uncertainty), with set-based representations offering a more intuitive approach.
• The classification of uncertainty as aleatoric or epistemic is relative to the chosen model of analysis, meaning context and feature space can shift how uncertainty is categorized.

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