A new paper challenges the standard definitions of epistemic uncertainty in machine learning, arguing that the common measure is inconsistent with the definition of uncertainty reducible by more data. The research proposes a revised taxonomy that distinguishes between sample-reducible and mechanism-reducible epistemic uncertainty. It also demonstrates that in-distribution data may not reduce, and can even increase, mechanism-irreducible uncertainty, suggesting that ensemble disagreement is a poor proxy for epistemic uncertainty. AI
IMPACT Challenges existing frameworks for understanding model uncertainty, potentially impacting how AI systems are evaluated and deployed.
RANK_REASON The cluster contains an academic paper discussing theoretical aspects of machine learning uncertainty.
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