As AI increasingly relies on synthetic data to supplement costly human-made content, researchers warn of potential instability and bias, but also recognize opportunities for targeted model improvement.
What is the impact of AI-generated data on AI models?
AI-generated data can significantly influence AI models by filling knowledge gaps, but it also poses risks such as 'model collapse,' where repeated training on synthetic data can lead to incoherent outputs. Research indicates that when a generative AI model was trained largely on AI-generated data, it eventually produced nonsensical responses due to the accumulation of errors over generations.
Can synthetic data improve fairness in AI models?
Yes, synthetic data can be tailored to address limitations in traditional datasets. Recent studies show that targeted sampling of AI-generated data can reduce harmful responses and improve fairness in AI models. However, there are concerns that reliance on synthetic data may lead to a loss of fairness, particularly for minority data, if not managed carefully.
How can AI-generated data be used effectively?
To use AI-generated data effectively, it is important to combine it 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 represent a broader range of human experiences.
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