While generative AI tools can help users with such tasks as brainstorming for new ideas, organizing existing information, mapping out scholarly discussions, or summarizing sources, they are also notorious for not relying fully on factual information or rigorous research strategies. In fact, they are known for producing "hallucinations," an AI science term used to describe false information created by the AI system to defend its statements. Oftentimes, these "hallucinations" can be presented in a very confident manner and consist of partially or fully fabricated citations or facts.
Certain AI tools have even been used to intentionally produce false images or audiovisual recordings to spread misinformation and mislead the audience. Referred to as "deep fakes," these materials can be utilized to subvert democratic processes and are thus particularly dangerous.
Additionally, the information presented by generative AI tools may lack currency as some of the systems do not necessarily have access to the latest information. Rather, they may have been trained on past datasets, thus generating dated representations of current events and the related information landscape.
Another potentially significant limitation of AI is the bias that can be embedded in the products it generates. Fed immense amounts of data and text available on the internet, these large language model systems are trained to simply predict the most likely sequence of words in response to a given prompt, and will therefore reflect and perpetuate the biases inherent in the inputted internet information. An additional source of bias lies in the fact that some generative AI tools utilize reinforcement learning with human feedback (RLHF), with the caveat that the human testers used to provide this feedback are themselves non-neutral. Accordingly, generative AI like ChatGPT is documented to have provided output that is socio-politically biased, occasionally even containing sexist, racist, or otherwise offensive information.