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Personalized uncertainty quantification in artificial intelligence

Nature Machine Intelligence volume 7, pages 522–530 (2025)Cite this article

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Abstract

Artificial intelligence (AI) tools are increasingly being used to help make consequential decisions about individuals. While AI models may be accurate on average, they can simultaneously be highly uncertain about outcomes associated with specific individuals or groups of individuals. For high-stakes applications (such as healthcare and medicine, defence and security, banking and finance), AI decision-support systems must be able to make personalized assessments of uncertainty in a rigorous manner. However, the statistical frameworks needed to do so are currently incomplete. Here, we outline current approaches to personalized uncertainty quantification (PUQ) and define a set of grand challenges associated with the development and use of PUQ in a range of areas, including multimodal AI, explainable AI, generative AI and AI fairness.

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Fig. 1: CP for PUQ.
Fig. 2: Grand challenges in PUQ.

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Acknowledgements

This work is supported by the Turing–Roche Strategic Partnership. C.R.S.B. was supported by the CRUK City of London Centre Award (CTRQQR-2021\100004). A.F.F. acknowledges support from the Royal Academy of Engineering under the RAEng Chair in Emerging Technologies (INSILEX CiET1919\/19), ERC Advanced Grant – UKRI Frontier Research Guarantee (INSILICO EP\/Y030494/1). A.F.F. also acknowledges the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC) (NIHR203308). T.C. is supported by a principal research fewllowship from the UCL Biomedical Research Centre (BRC).

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Authors and Affiliations

  1. The Alan Turing Institute, London, UK

    Tapabrata Chakraborti, Christopher R. S. Banerji, Ariane Marandon, Vicky Hellon, Deepak Parashar & Ben MacArthur

  2. University College London, London, UK

    Tapabrata Chakraborti, Christopher R. S. Banerji, Robin Mitra, Brieuc Lehmann & Leandra Bräuninger

  3. King’s College London, London, UK

    Christopher R. S. Banerji

  4. Genentech, South San Francisco, CA, USA

    Sarah McGough

  5. University of Warwick, Coventry, UK

    Cagatay Turkay & Deepak Parashar

  6. University of Manchester, Manchester, UK

    Alejandro F. Frangi

  7. Queen Mary University, London, UK

    Ginestra Bianconi

  8. Henley Business School, University of Reading, Reading, UK

    Weizi Li

  9. University of Southampton, Southampton, UK

    Owen Rackham & Ben MacArthur

  10. Roche Pharmaceuticals, Welwyn Garden City, UK

    Chris Harbron

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Chakraborti, T., Banerji, C.R.S., Marandon, A. et al. Personalized uncertainty quantification in artificial intelligence. Nat Mach Intell 7, 522–530 (2025). https://doi.org/10.1038/s42256-025-01024-8

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