“That process of creating a synthetic data set, depending on what you’re extrapolating from and how you’re doing that, can actually exacerbate the biases,” says Deb Raji, a technology fellow at the AI Now Institute. “Synthetic data can be useful for assessment and evaluation [of algorithms], but dangerous and ultimately misleading when it comes to training [them].”