Synthetic Data in AI: Challenges, Applications, and Ethical Implications

TL;DR

The paper addresses how synthetic data can mitigate real-data limitations while introducing new biases and ethical risks. It surveys generation methods spanning statistical modeling, VAEs, GANs, diffusion models, and LLM-based approaches, and reviews applications across vision, audio, NLP, and health. It discusses data-distribution issues, biases in outputs, and the need for transparency and regulation to ensure fair, robust AI systems. The work aims to guide practitioners and policymakers toward responsible use of synthetic data, balancing utility with safety, privacy, and fairness considerations.

Abstract

In the rapidly evolving field of artificial intelligence, the creation and utilization of synthetic datasets have become increasingly significant. This report delves into the multifaceted aspects of synthetic data, particularly emphasizing the challenges and potential biases these datasets may harbor. It explores the methodologies behind synthetic data generation, spanning traditional statistical models to advanced deep learning techniques, and examines their applications across diverse domains. The report also critically addresses the ethical considerations and legal implications associated with synthetic datasets, highlighting the urgent need for mechanisms to ensure fairness, mitigate biases, and uphold ethical standards in AI development.