AI Peers Exert Social Influence on Human Dishonesty in Groups

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 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.

Introduction. Artificial Intelligence (AI) systems are increasingly integrated into collaborative environments, transitioning from passive support tools to active peers and decision-makers [19, 68]. Consequently, collective decision-making is evolving from human-only settings to human-AI group decisions [19, 59]. In these environments, AI agents participate alongside humans, share task contexts, and express explicit recommendations or perform observable actions [23, 59, 73]. This shift alters group dynamics and establishes a new social landscape where collective norms and collaborative decisions are negotiated between humans and AI. However, the presence of AI peers introduces critical challenges when these AI exhibit unethical actions, such as dishonest behavior. We argue that AI misconduct can alter human ethical behavior through three interconnected mechanisms. First, from a technical perspective, autonomous AI agents can generate self-serving or dishonest outputs when following specific reward functions [40, 44].

Discussion / Conclusion. 5 Discussions 5.1 AI Agents and Their Social Influence Our findings advance HCI community’s understanding of unethical conduct in mixed human-AI teams. We contextualize our contributions from AI’s social influence to its interpretation, and further to the characteristics of such influence. First, we advance the conceptualization of AI in ethical decision making by showing its efficacy as an active social influencer. Prior frameworks, such as Kobis et al. [40, 41], primarily examined AI as an enabler by showing how humans delegate dishonest tasks to automated systems. Our studies address the role of AI as an influencer and an advisor. We show that AI agents acting as group peers directly shape human choices through social norms. Across descriptive, injunctive, and subjective norm conditions, AI agents communicate standards of behavior that participants actively follow. In descriptive settings, as an influencer, AI peers show specific reporting actions that humans reproduce.