Synthetic Data Generation with Generative AI for Low-Resource Predictive Modeling
Keywords:
synthetic data, generative AI, low-resource predictive modeling, data augmentation, fairness, robustness, governance, infrastructureAbstract
The increasing demand for predictive models in domains where labeled data are scarce has motivated the exploration of synthetic data generation using generative artificial intelligence. This paper presents a comprehensive systems-level analysis of the architectures, trade-offs, and governance challenges inherent in deploying generative models for low-resource predictive modeling. We examine the structural properties of generative adversarial networks, variational autoencoders, and diffusion models as they relate to data fidelity, diversity, and privacy preservation. The discussion extends to the infrastructure required for training such models on limited real data, including transfer learning, self-supervised pretraining, and federated setups. Critical tensions between statistical utility and fairness are analyzed, particularly the risk of amplifying biases present in small real-world samples. Policy implications, including regulatory frameworks for synthetic data provenance and accountability, are explored through cross-domain case illustrations in healthcare, finance, and natural language processing. The paper concludes with forward-looking perspectives on sustainable deployment, robustness evaluation, and the need for standardized benchmarks. Our findings suggest that while generative AI offers a powerful pathway to overcome data scarcity, its success depends on careful architectural choices, rigorous validation protocols, and governance structures that ensure equitable outcomes across populations.
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