Generative AI can create novel outputs and also cause AI feedback loops that can lead to undesired outcomes.

Powerful generative AI models are now able to produce realistic and innovative material. However, these models may apply harmful material, such as deep fakes or fake news, because generative models can produce content that reflects bias after being trained on partial or damaging data. A feedback loop is one method via which this may take place. Use a  generative model's output to train the model, producing additional output identical to the initial output in a feedback loop. As a result of just being exposed to data that reflects its own biases, the model may become more biased or destructive.

For instance, a generative model that has been educated or trained on a dataset of fake news stories is likely to produce more fake news due to its ability to identify patterns indicative of fake news and its subsequent ability to produce content that mimics those patterns. Researchers strive to create strategies to reduce these hazards since AI feedback loops could be dangerous. One method to accomplish this is to eliminate bias by training generative models on a more varied dataset. Researchers are also working on techniques to identify and eliminate hazardous information from generative models.

Generative AI models have the potential to be practical tools for good despite the concerns. Researchers can contribute to ensuring the proper utilization of generative AI models for beneficial purposes by creating techniques to reduce the risks of AI feedback loops.