AI peers influence human dishonesty in groups like humans do
AI Peers Exert Social Influence on Human Dishonesty in Groups
Human-Computer Interaction
Summary
People tend to be dishonest in groups, especially when others around them act dishonestly. This study found that AI group members influence people’s honesty just as much as other humans do. When AI or human peers show dishonest behavior, people are more likely to be dishonest too. The biggest change happens when the first dishonest peer appears, while adding more dishonest peers has less impact. This shows the importance of carefully managing how AI behaves in groups to avoid encouraging dishonesty.
What this means in practice
- •For human resources teams: Design workplace policies considering how AI colleagues may impact employee honesty in group tasks.
- •For ai system developers: Create AI group members that promote honest behavior by controlling normative signals in collaborative settings.
Authors
Shuning Zhang, Xinyuan Zhou, Yuanyang Qiu, Tianqi Song, Yuting Yang, Yiwen Ren, Xin Yi
Abstract
Human dishonesty in group settings is highly susceptible to peer influence, particularly when incentivized. Although artificial intelligence (AI) evolves from passive tools into active collaborators, its impact on human moral behavior within groups remains underexplored. We addressed this gap through a two-phase randomized behavioral study (N=280 and N=360). We found AI agents exert substantial social influence comparable in magnitude to that of human peers. Specifically, participants reported more dishonestly when exposed to dishonest rather than honest normative cues. This effect is evident across injunctive, subjective, and descriptive social norms. Interestingly, the only significant adjacent behavioral change occurred when dishonest peer behavior first appeared, whereas further increases from one to four dishonest peers produced weaker and non-monotonic changes. Furthermore, participants rapidly converge on decision-making, showing modest increases in dishonest reporting through repeated exposure. These findings highlight the importance of managing the behaviors and normative signals communicated by AI group members.