AI-generated data can significantly affect AI models by filling knowledge gaps but also poses risks such as 'model collapse,' where repeated training on synthetic data leads to incoherent outputs. Research indicates that when a generative AI model was trained primarily on AI-generated data, it eventually produced nonsensical responses after several iterations.

Yes, synthetic data can be tailored to improve fairness in AI models. Recent studies show that targeted sampling of AI-generated data can reduce harmful responses and enhance representation. However, there are concerns that reliance on synthetic data may lead to a loss of fairness, particularly for minority data that is less frequently represented.

To use AI-generated data effectively, it should be combined with high-quality human-generated data. Retaining a portion of original human data during training can help maintain model performance and prevent collapse. Additionally, using data from a diverse set of sources can mitigate risks and enhance the model's ability to produce reliable outputs.