AI Uncertainty Quantification: Taxonomy, Validation, and Trustworthiness

A new paper on arXiv, "Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation," by Ramon Winterhalder and others, provides a structured overview of uncertainty quantification in machine learning for physics applications. It introduces a unified taxonomy for uncertainty, distinguishing between predictive and inference uncertainties within frequentist and Bayesian frameworks. The paper also details validation tools like coverage, calibration, and proper scoring rules, illustrating their use with regression and classification examples. Concurrently, a dev.to post titled "Uncertainty Quantification in AI (Part - 1)" by Karthik Amirapu discusses the critical need for AI systems to express confidence in their predictions, likening poorly calibrated models to unreliable friends. This post introduces the concepts of aleatoric uncertainty (inherent randomness) and epistemic uncertainty (lack of knowledge), explaining how distinguishing between them is crucial for building trustworthy AI. AI

IMPACT Enhances the trustworthiness of AI systems by providing methods to quantify and validate prediction uncertainty, crucial for high-stakes applications.

RANK_REASON The cluster focuses on an academic paper and a related technical blog post discussing uncertainty quantification in AI, a core research topic.

Read on arXiv stat.ML →

• aleatoric uncertainty
• epistemic uncertainty
• machine learning
• uncertainty quantification
• arXiv
• Karthik Amirapu
• physics
• Ramon Winterhalder
• Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation

• paper
• other

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