Exploring Persuasive Engagement to Reduce Over-Reliance on AI-Assistance in a Customer Classification Case
Muhammad Raees, Vassilis-Javed Khan, Konstantinos Papagelis · 2025
Users often over-rely on AI-assisted decisions without analytically engaging with them, even in practical domains.In this work, we explore persuading users to analytically engage with AI assistance to reduce their over-reliance using a complex business case of customer classification.We explore the effect of persuasive cognitive engagement through explanations and communicating system uncertainty to examine the behavior of participants having diverse expertise.We leverage their feedback and objective behavior to understand their perception of the AI performance.Our findings show a contrast in participants' subjective and objective behavior, indicating inappropriate reliance on AI assistance with the perception of system performance.However, we observe the positives of interactive cognitive engagement and identify further directions to get deeper insights into expert domains with personalized AI assistance and behavioral persuasion.