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

• URL: http://arxiv.org/abs/2401.01629v1
• Date: Wed, 3 Jan 2024 09:03:30 GMT
• Title: Synthetic Data in AI: Challenges, Applications, and Ethical Implications
• Authors: Shuang Hao, Wenfeng Han, Tao Jiang, Yiping Li, Haonan Wu, Chunlin Zhong, Zhangjun Zhou, He Tang
• Abstract summary: This report explores the multifaceted aspects of synthetic data. It emphasizes the challenges and potential biases these datasets may harbor. It also critically addresses the ethical considerations and legal implications associated with synthetic datasets.
• Score: 16.01404243695338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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