HackerPapers

arXiv:2302.00560cs.HC

Co-Writing with Opinionated Language Models Affects Users' Views

Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, Mor Naaman

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

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.

arXiv comments
Journal reference
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23-28, 2023, Hamburg, Germany. ACM, New York, NY, USA
DOI
doi:10.1145/3544548.3581196
Subjects
cs.HCHuman-Computer Interaction cs.AIArtificial Intelligence cs.CLComputation and Language

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Feb 2023 to Feb 2023. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.