arXiv:2606.26361v1 Announce Type: new Abstract: ML foundation models are able to emulate atmospheric dynamics accurately and efficiently but operate as opaque ``black boxes''. We investigate the internal representations of the Aurora model using spatially pooled PCA and layer-wise relevance propagation (LRP). We find evidence that Aurora's latent space is primarily organized by seasonal cycles, whereas extreme storm events do not form a linearly separable cluster. LRP indicates that the model attends to features consistent with the 3D vertical structure of the Great Storm of 1987. Perturbation
This research provides crucial insight into the interpretability and internal mechanisms of large AI models, particularly in the context of scientific emulation. It addresses the 'black box' problem as AI models become more ubiquitous in critical applications like climate modeling.
Understanding how AI models like Aurora interpret and encode complex physical phenomena is vital for trusting their predictions and integrating them into scientific discovery and policy-making. It moves beyond raw accuracy to model explainability and reliability.
The ability to attribute internal representations to specific physical structures, such as atmospheric layers or storm events, begins to validate AI as a scientific tool rather than just a predictive engine. This changes how researchers can verify and refine AI models for scientific use.
- · AI interpretability researchers
- · Climate scientists
- · AI model developers
- · Atmospheric science sector
- · Developers of entirely opaque AI models
- · Skeptics of AI's scientific utility
Increased confidence in using advanced AI models for emulating and predicting complex physical systems, accelerating scientific discovery.
Development of regulatory or ethical guidelines demanding interpretability for AI models used in critical scientific or societal applications.
New AI architectures designed from the ground up for interpretability and explainability, becoming a core feature rather than an afterthought, which could inform broader AI development.
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Read at arXiv cs.LG