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.
