Synthetic Data in AI: Challenges, Applications, and Ethical Implications
Shuang Hao Wenfeng Han Tao Jiang Yiping Li Haonan Wu Chunlin Zhong Zhangjun Zhou He Tang
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.