Synthetic Data Quality Evaluation in Generative AI: Current Trends, Challenges, and Future Directions for Social Science Research

Abstract

Generative Artificial Intelligence (GenAI) has transformed data generation through advanced models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and Large Language Models (LLMs), enabling the creation of synthetic datasets that closely resemble real-world data while addressing challenges related to privacy, accessibility, and regulatory compliance. As synthetic data becomes increasingly adopted across healthcare, education, finance, public administration, and social science research, ensuring its quality, reliability, fairness, and trustworthiness has emerged as a critical research priority. This literature review examines recent developments in synthetic data quality evaluation between 2020 and 2026, focusing on key dimensions including utility, fidelity, privacy preservation, fairness, diversity, robustness, interpretability, and governance. The review traces the evolution of evaluation methodologies from traditional statistical similarity measures toward multidimensional assessment frameworks such as SynEval, SynthEval, Benchmarking Synthetic Tabular Data Framework, SynAE, and ESDAE. A structured comparison of these frameworks is presented using criteria including analytical accuracy, scalability, privacy protection, fairness assessment, and interpretability. The review further explores emerging approaches for explainable synthetic data assessment, fairness-aware synthetic data generation, and Privacy-Enhancing Technologies (PETs), including differential privacy, federated learning, secure multi-party computation, and privacy-preserving generative models. In addition, real-world case studies from healthcare, education, public policy, and social science research are examined to demonstrate how synthetic data quality evaluation directly influences decision-making, research validity, and policy outcomes. The findings indicate that while modern generative models can produce highly realistic and analytically useful datasets, persistent challenges remain, including the lack of standardized benchmarking protocols, utility–privacy trade-offs, privacy leakage risks, bias amplification, limited explainability, and governance concerns. The review concludes that future research should prioritize internationally accepted evaluation standards, explainable and fairness-aware assessment frameworks, stronger privacy-preserving mechanisms, and comprehensive governance models to support the responsible, transparent, and trustworthy deployment of synthetic data in the Generative AI era.