Polarization of Autonomous Generative AI Agents Under Echo Chambers
Masaya Ohagi
Online social networks often create echo chambers where people only hear
opinions reinforcing their beliefs. An echo chamber often generates
polarization, leading to conflicts caused by people with radical opinions, such
as the January 6, 2021, attack on the US Capitol. The echo chamber has been
viewed as a human-specific problem, but this implicit assumption is becoming
less reasonable as large language models, such as ChatGPT, acquire social
abilities. In response to this situation, we investigated the potential for
polarization to occur among a group of autonomous AI agents based on generative
language models in an echo chamber environment. We had AI agents discuss
specific topics and analyzed how the group's opinions changed as the discussion
progressed. As a result, we found that the group of agents based on ChatGPT
tended to become polarized in echo chamber environments. The analysis of
opinion transitions shows that this result is caused by ChatGPT's high prompt
understanding ability to update its opinion by considering its own and
surrounding agents' opinions. We conducted additional experiments to
investigate under what specific conditions AI agents tended to polarize. As a
result, we identified factors that strongly influence polarization, such as the
agent's persona. These factors should be monitored to prevent the polarization
of AI agents.