Generative AI models take a vast amount of content from across the internet and then use the information they are trained on to make predictions and create an output for the prompt you input. These predictions are based off the data the models are fed, but there are no guarantees the prediction will be correct, even if the responses sound plausible.
The responses might also incorporate biases inherent in the content the model has ingested from the internet, but there is often no way of knowing whether that’s the case. Both of these shortcomings have caused major concerns regarding the role of generative AI in the spread of misinformation.
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Generative AI models don’t necessarily know whether the things they produce are accurate, and for the most part, we have little way of knowing where the information has come from and how it has been processed by the algorithms to generate content.
There are plenty of examples of chatbots, for example, providing incorrect information or simply making things up to fill the gaps. While the results from generative AI can be intriguing and entertaining, it would be unwise, certainly in the short term, to rely on the information or content they create.
Some generative AI models, such as Bing Chat or GPT-4, are attempting to bridge that source gap by providing footnotes with sources that enable users to not only know where their response is coming from, but to also verify the accuracy of the response.
