WSJ (free link) – Help! My Political Beliefs Were Altered by a Chatbot! AI assistants may be able to change our views without our realizing it. Says one expert: ‘What’s interesting here is the subtlety.’ When we ask ChatGPT or another bot to draft a memo, email, or presentation, we think these artificial-intelligence assistants are doing our bidding. A growing body of research shows that they also can change our thinking—without our knowing. One of the latest studies in this vein, from researchers spread across the globe, found that when subjects were asked to use an AI to help them write an essay, that AI could nudge them to write an essay either for or against a particular view, depending on the bias of the algorithm. Performing this exercise also measurably influenced the subjects’ opinions on the topic, after the exercise. “You may not even know that you are being influenced,” says Mor Naaman, a professor in the information science department at Cornell University, and the senior author of the paper. He calls this phenomenon “latent persuasion.” These studies raise an alarming prospect: As AI makes us more productive, it may also alter our opinions in subtle and unanticipated ways. This influence may be more akin to the way humans sway one another through collaboration and social norms, than to the kind of mass-media and social media influence we’re familiar with..”

See also Whose Opinions Do Language Models Reflect? 30 Mar 2023. Language models (LMs) are increasingly being used in open-ended contexts, where the opinions reflected by LMs in response to subjective queries can have a profound impact, both on user satisfaction, as well as shaping the views of society at large. In this work, we put forth a quantitative framework to investigate the opinions reflected by LMs — by leveraging high-quality public opinion polls and their associated human responses. Using this framework, we create OpinionsQA, a new dataset for evaluating the alignment of LM opinions with those of 60 US demographic groups over topics ranging from abortion to automation. Across topics, we find substantial misalignment between the views reflected by current LMs and those of US demographic groups: on par with the Democrat-Republican divide on climate change. Notably, this misalignment persists even after explicitly steering the LMs towards particular demographic groups. Our analysis not only confirms prior observations about the left-leaning tendencies of some human feedback-tuned LMs, but also surfaces groups whose opinions are poorly reflected by current LMs (e.g., 65+ and widowed individuals). Our code and data are available at this https URL.”