TY  - JOUR
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
IS  - 2
EP  - 114
AV  - restricted
VL  - 18
Y1  - 2025/02//
SP  - 112
TI  - Explainability can foster trust in artificial intelligence in geoscience
A1  - Dramsch, Jesper Sören
A1  - Kuglitsch, Monique M
A1  - Fernández-Torres, Miguel-Ángel
A1  - Toreti, Andrea
A1  - Albayrak, Rustem Arif
A1  - Nava, Lorenzo
A1  - Ghaffarian, Saman
A1  - Cheng, Ximeng
A1  - Ma, Jackie
A1  - Samek, Wojciech
A1  - Venguswamy, Rudy
A1  - Koul, Anirudh
A1  - Muthuregunathan, Raghavan
A1  - Hrast Essenfelder, Arthur
JF  - Nature Geoscience
UR  - https://doi.org/10.1038/s41561-025-01639-x
PB  - Springer Science and Business Media LLC
ID  - discovery10205986
N2  - 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.
ER  -