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Explainability can foster trust in artificial intelligence in geoscience

Nature Geoscience volume 18, pages 112–114 (2025)Cite this article

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Uptake of explainable artificial intelligence (XAI) methods in geoscience is currently limited. We argue that such methods that reveal the decision processes of AI models can foster trust in their results and facilitate the broader adoption of AI.

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Fig. 1: The value of explainable artificial intelligence (XAI).

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Acknowledgements

We are appreciative of Chelsea Shu’s first investigations of the natural language processing tool for exploring the scientific literature and the collaboration and insight provided by use case proponents of the Focus Group on AI for Natural Disaster Management (https://www.itu.int/en/ITU-T/focusgroups/ai4ndm/Pages/default.aspx).

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Authors and Affiliations

  1. European Centre for Medium-Range Weather Forecasts, Bonn, Germany

    Jesper Sören Dramsch

  2. Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany

    Monique M. Kuglitsch, Ximeng Cheng, Jackie Ma & Wojciech Samek

  3. Image Processing Laboratory (IPL), Universitat de València (UV), Paterna, (València), Spain

    Miguel-Ángel Fernández-Torres

  4. European Commission Joint Research Centre, Ispra, Italy

    Andrea Toreti & Arthur Hrast Essenfelder

  5. NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Rustem Arif Albayrak

  6. University of Maryland, Baltimore County, MD, USA

    Rustem Arif Albayrak

  7. Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padova, Padova, Italy

    Lorenzo Nava

  8. Department of Earth Sciences, University of Cambridge, Cambridge, UK

    Lorenzo Nava

  9. Department of Geography, University of Cambridge, Cambridge, UK

    Lorenzo Nava

  10. Department of Risk and Disaster Reduction, University College London, London, UK

    Saman Ghaffarian

  11. Technical University of Berlin, Berlin, Germany

    Wojciech Samek

  12. BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany

    Wojciech Samek

  13. Pinterest, San Francisco, CA, USA

    Rudy Venguswamy & Anirudh Koul

  14. LinkedIn, Mountain View, CA, USA

    Raghavan Muthuregunathan

Authors
  1. Jesper Sören Dramsch
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  2. Monique M. Kuglitsch
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  4. Andrea Toreti
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  6. Lorenzo Nava
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  7. Saman Ghaffarian
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  8. Ximeng Cheng
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  9. Jackie Ma
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  10. Wojciech Samek
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  13. Raghavan Muthuregunathan
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Correspondence to Jesper Sören Dramsch.

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The authors declare no competing interests.

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Dramsch, J.S., Kuglitsch, M.M., Fernández-Torres, MÁ. et al. Explainability can foster trust in artificial intelligence in geoscience. Nat. Geosci. 18, 112–114 (2025). https://doi.org/10.1038/s41561-025-01639-x

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