2024/08/10 by Richard Timpone, Timpone, Richard, Yongwei Yang +1
Business, Management and Accounting · Decision Sciences · #Big Data and Business Intelligence #Computers and Society (cs.CY) #Data Quality and Management #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Methodology (stat.ME) #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2408.15260
openalex publication_date 2024/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Synthetic Data is not new, but recent advances in Generative AI have raised interest in expanding the research toolbox, creating new opportunities and risks. This article provides a taxonomy of the full breadth of the Synthetic Data domain. We discuss its place in the research ecosystem by linking the advances in computational social science with the idea of the Fourth Paradigm of scientific discovery that integrates the elements of the evolution from empirical to theoretic to computational models. Further, leveraging the framework of Truth, Beauty, and Justice, we discuss how evaluation criteria vary across use cases as the information is used to add value and draw insights. Building a framework to organize different types of synthetic data, we end by describing the opportunities and challenges with detailed examples of using Generative AI to create synthetic quantitative and qualitative datasets and discuss the broader spectrum including synthetic populations, expert systems, survey data replacement, and personabots.