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AI technologies such as Large Language Models (LLMs) are increasingly used to make suggestions to autocomplete text as people write. Can these suggestions impact people’s writing and attitudes? In two large-scale preregistered experiments (N=2,582), we expose participants who are writing about important societal issues to biased AI-generated suggestions. The attitudes participants expressed in their writing and 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, awareness of the task or of the AI’s bias, e.g. warning participants about potential bias before or after exposure to the treatment, did not mitigate the influence effect. Moreover, the AI’s influence is not fully explained by the additional information provided by the suggestions. 

If large language models like GPT-3 preferably produce a particular point of view, they may influence people’s opinions on an unknown scale. This study investigates whether a language-model-powered writing assistant that generates some opinions more often than others impacts what users write – and what they think. In an online experiment, we asked participants (N=1,506) to write a post discussing whether social media is good for society. Treatment group participants used a language-model-powered writing assistant configured to argue that social media is good or bad for society. Participants then completed a social media attitude survey, and independent judges (N=500) evaluated the opinions expressed in their writing. Using the opinionated language model affected the opinions expressed in participants’ writing and shifted their opinions in the subsequent attitude survey. We discuss the wider implications of our results and argue that the opinions built into AI language technologies need to be monitored and engineered more carefully.