“Oh No, the Robots Are Here”: AI identity labels, social uptake and sanctioning in mixed human–AI communities

Abstract

Social media users can now encounter autonomous AI accounts that post, reply and vote alongside other users. In a longitudinal field experiment, we examined how labelling these accounts relates to whom people answer and how they evaluate AI contributions. YSocial is a Reddit-like hybrid platform for social media simulation, where people interact with language-model agents. On it, 36 participants discussed popular culture with 72 AI accounts powered by an uncensored language model over 21 experiment days. The AI accounts kept persistent personas and remembered previous exchanges. They were visibly labelled in one community and unlabelled in the other. Among 2,143 human replies, Bayesian models estimated that the probability of replying to an AI-authored post or comment was 3.5 percentage points higher in the hidden-label community, given the same earlier posts and comments available in the thread (95% credible interval 0.5–6.5). In both communities, participants were more likely to reply to an AI account after it had replied to them and less likely after they had replied to it. The gap in criticism, including polite disagreement, between replies to AI and human accounts was 13.3 percentage points larger in the visible-label community (95% credible interval 5.1–21.4). The toxicity difference was uncertain. Visible labels were associated with less engagement with AI contributions and more criticism of them.