Machine learning uncertainty evaluation could adopt measurement science principles

A new paper proposes a framework for evaluating uncertainty in machine learning (ML) predictions by drawing parallels with measurement science and metrology. The authors argue that the current ML dichotomy of aleatoric and epistemic uncertainty is insufficient, similar to how traditional distinctions between random and systematic measurement errors were inadequate. They advocate for a metrology-inspired approach that systematically identifies, quantifies, and propagates uncertainty contributions to provide a more rigorous and consistent evaluation of prediction trustworthiness. AI

IMPACT Proposes a more rigorous and consistent framework for evaluating the trustworthiness of machine learning predictions, potentially improving safety in critical applications.

RANK_REASON The cluster contains a research paper published on arXiv proposing a new framework for uncertainty evaluation in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Guide to the Expression of Uncertainty in Measurement
• Hugging Face
• IArxiv
• machine learning
• Measurement Science and Technology
• metrology
• ScienceCast

• paper
• safety

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