Confirmation Bias in Generative AI Chatbots: Mechanisms, Risks, Mitigation Strategies, and Future Research Directions

Drawing on cognitive psychology and computational linguistics, it examines how confirmation bias--commonly understood as the tendency to seek information that aligns with existing beliefs--can be replicated and amplified by the design and functioning of large language models. The article analyzes the mechanisms by which confirmation bias may manifest in chatbot interactions, assesses the ethical and practical risks associated with such bias, and proposes a range of mitigation strategies. These include technical interventions, interface redesign, and policy measures aimed at promoting balanced AI-generated discourse. The article concludes by outlining future research directions, emphasizing the need for interdisciplinary collaboration and empirical evaluation to better understand and address confirmation bias in generative AI systems. Keywords: confirmation bias, generative AI, chatbots, large language models, AI ethics, user interaction 1. Introduction The emergence of generative AI chatbots has marked a significant turning point in the field of artificial intelligence (AI) (Chang et al., 2 0 2 4). These systems, underpinned by large-scale language models, have demonstrated a remarkable capacity for producing coherent, contextually relevant, and often creative responses to human queries (Wang et al., 2 0 2 4).