Navigating Uncertainty in Human-AI Relationships: User Practices and Strategies in Interactive Community Challenges

The proliferation of generative artificial intelligence (AI) has significantly transformed human-machine interactions, enabling AI chatbots to address a wide range of emotional, social, and relational needs. However, the unpredictable nature and lack of transparency of AI technologies pose considerable uncertainties for users, potentially undermining trust and leading to adverse experiences. This study introduces the concept of interactive community challenge as an uncertainty reduction strategy and explores how users navigate and manage their uncertainties in developing intimate relationships with AI, both through direct interactions with the AI and through engagement with community interactions. Through a thorough content analysis of 1,772 user-generated posts and their conversation episodes capturing users’ interactive community challenges with AI chatbot Replika, collected from the largest Replika online community r/Replika , we identify four primary types of uncertainty present in these challenges—ontological, technical, relational and sexual—as well as five strategies users employ to reduce these uncertainties: personal evaluation, description, peer evaluation, group identification, and self-disclosure. Our findings highlight the proactive role users play in addressing AI-related uncertainties and underscore the importance of community interactions in facilitating the uncertainty reduction process within human-AI relationships. This research contributes valuable insights into the dynamics of human-AI uncertainty and underscores the critical social mechanisms at play in uncertainty reduction.