Rethinking Cognitive Bias in Human–AI Cognitive Systems: A Structured Conceptual Mapping Review

Research in cognitive psychology has long shown that human judgment and decision making are systematically influenced by cognitive biases, particularly under conditions of uncertainty. As artificial intelligence (AI) systems become increasingly embedded in socially relevant decision making contexts—such as healthcare, education, communication, and governance—these cognitive dynamics do not disappear. Instead, they may be reproduced, amplified, or transformed through human–AI interaction. Despite the rapid growth of research on cognitive biases in AI-related contexts, existing reviews remain fragmented and heterogeneous, often focusing on single biases or specific application domains. This article reports a structured conceptual mapping review of review-level publications examining how cognitive biases are conceptualized, categorized, and addressed in human–AI interaction. A bounded search of Scopus, Web of Science, and PubMed identified ten eligible review publications published between 2020 and 2025. Using a three-level framework, the mapping distinguishes data-level, algorithmic-level, and human-interaction-level processes. Within the retrieved corpus, the analysis reveals heterogeneous terminology, a predominance of healthcare applications and interaction-level analyses, and limited review-level synthesis of empirical evaluations of mitigation strategies. Drawing on distributed cognition and joint cognitive systems, we propose coupled human–AI cognition as a theoretical lens rather than as an empirically established conclusion. The study provides a structured conceptual map, identifies terminological and methodological gaps, and develops propositions for future empirical research.