Synthetic data promises faster, cheaper, and more flexible research � but it comes with risks. Using frameworks such as Truth, Transparency, and Trust, and blending Human and Artificial Intelligence, safe outcomes can be achieved. This paper explores the do�s and don�ts of synthetic augmentation, then presents an empirical study with a Digital Twin Panel, where AI-generated �clones� of real respondents are tested in choice studies, examining whether digital twins can represent the next generation of research automation.