Synthetic data is useful for AI training but has limitations. Over-reliance on it can lead to model collapse, bias amplification, and a failure to capture real-world complexities. This can erode trust in AI systems and stifle innovation. The article suggests a balanced approach, combining synthetic and human-sourced data, along with tools for data provenance and AI-powered filters. Partnering with trusted data providers and promoting digital literacy are also crucial for responsible AI development.

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