Cascade Effect in AI-Human Interactions: Study of Cognitive Biases, Personality Traits and Decision Making Patterns

Abstract This qualitative study examines the cascade effect in artificial intelligence (AI)-human interactions, where repeated exposure to AI predictions creates feedback loops that incrementally influence human decision-making. Through purposive sampling, the research engages 15 young adults (aged 18–30) who regularly use AI systems, employing semi-structured interviews to explore how this cascade effect manifests in daily decision-making processes. The study utilizes thematic analysis and enables the investigation of how cognitive biases (anchoring, confirmation, and automation) develop and cascade through repeated AI interactions and how personality traits, particularly assertiveness and agreeableness, moderate this cumulative influence. The research aims to uncover patterns in trust development, bias awareness, and AI reliance over time while identifying effective strategies for maintaining autonomous decision-making capabilities. The study revealed that cognitive bias creates a cascade effect in AI-human interaction, where cognitive bias affects trust and dependency, with assertiveness moderating the bias-trust relationship and agreeableness moderating the bias-dependency relationship Keywords: cascade effect, cognitive bias, human-AI interaction, personality traits, decision-making.