AI writing assistants powered by Large Language Models (LLMs) are increasingly used to make autocomplete suggestions to people as they write. Can these AI writing assistants impact people’s attitudes in this process? In two large-scale preregistered experiments (N=2,582), we exposed participants writing about important societal issues to an AI writing assistant that provided biased autocomplete suggestions. When using the AI assistant, the attitudes participants expressed in a post-task survey converged towards the AI’s position. Yet, a majority of participants were unaware of the AI suggestions’ bias and their influence. Further, the influence of the AI writing assistant was stronger than the influence of its suggestions when presented as static text, showing that the influence is partially due to AI writing assistants leading people to incorporate biased viewpoints into their own writing. Lastly, warning participants about assistants’ bias before or after exposure does not mitigate the influence effect.
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Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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