Seeing the Hivemind: A Consensus-Aware Interaction Technique for Mitigating AI Homogenization

Abstract

People are increasingly using AI for creative tasks such as writing. While adoption continues to grow, this form of use risks undermining individual creativity locally and reducing the heterogeneity of creative output at scale. In response, we introduce the Semantic Repulsion Technique (SRT) and evaluate it both computationally and through a study with 16 participants who regularly use AI for creative tasks. Our computational assessment reveals that SRT increases semantic diversity by 85--167\% while reducing consensus phrases by 43--95\% across task modes. In the user study, SRT outputs received higher usefulness ($p = .019$, $W = .208$) and coherence ratings ( $p = .006$, $W = .260$); 68.8\% of participants were willing to use SRT-Strong for multiple tasks versus 18.8\% for baselines. Originality and coherence ratings were positively correlated across all systems ($ρ= +.40$ to $+.67$), suggesting that divergence need not compromise readability. Taken together, these preliminary findings can inform the design of AI systems that aim to support everyday creativity without contributing to homogenization.