Preface to 'advancing uncertainty quantification in artificial intelligence systems using conformal prediction'.

Authors

Khuong An Nguyen, Rina Foygel Barber, Vicky Copley, Alex Gammerman, Vladimir Vovk, Johanna Ziegel

Published in

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences. Volume 384. Issue 2327. Aug 27, 2026.

Abstract

As artificial intelligence (AI) systems are being widely deployed in safety-critical and high-stakes applications (e.g. medical diagnosis, autonomous vehicles, financial risk assessment), there is a growing demand for providing reliable and trustworthy machine predictions. However, since AI models become more complex in structure (a prominent example being deep neural networks) and bigger in size (e.g. large language model systems such as ChatGPT and Gemini), being able to understand, explain and quantify confidence in their predictions are ongoing challenges. Therefore, this special issue is dedicated to exploring the forefront of reliable uncertainty quantification in AI systems, using conformal prediction (CP), a leading statistical framework that offers predictions with valid coverage guarantees under minimal assumptions. The issue comprises the most recent, most novel and most practical developments of CP-based methods in cutting-edge AI applications, highlighting improvements over traditional methods.

PMID:
42656163
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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