The creation of plausible, factually-grounded data for training of machine models rather than, or in addition to, importing real-world data. Synthetic data use is intended to reduce bias, quickly train models, and improve accuracy. For example, synthesizing demographically-accurate data about the population of a university might be preferable to risking leaks of individuals' real addresses, grades, or other private information.
"The credit-scoring firm introduced synthetic data that corrected for inherited privilege, to counteract societal biases against women."
