The Intersection of AI and Behavioral Economics in Decision-Making

Abstract

This study explores the intersection of Artificial Intelligence (AI) and behavioral economics in decision-making, aiming to understand how AI influences human decision-making processes, mitigates or exacerbates cognitive biases, and builds trust among users. This qualitative research involved semi-structured interviews with 29 participants, recruited from various online platforms. The study aimed for theoretical saturation, and the data were analyzed using NVivo software. The participants were selected based on their familiarity with AI-driven decision-making systems, with a focus on their experiences, perceptions, and behavioral responses to AI interventions. The interviews covered themes such as the perceived reliability of AI, human biases in decision-making, trust in AI, and the impact of AI on decision outcomes. The study revealed that participants viewed AI systems as valuable tools in decision-making, particularly in contexts involving complex data. AI was seen as reducing cognitive biases like overconfidence and framing effects, although it also heightened trust in machine recommendations, which could lead to over-reliance. Transparency and explainability were identified as key factors in fostering trust in AI systems. However, concerns were raised about AI perpetuating existing biases, particularly when the underlying data were flawed or incomplete. Trust in AI was stronger when participants had access to clear explanations of AI’s decision-making process, emphasizing the importance of explainability in promoting user confidence. AI has the potential to enhance decision-making by reducing certain cognitive biases, but it also introduces new challenges, such as over-reliance and the amplification of existing biases. Future research should explore long-term effects and ways to design AI systems that minimize bias while maximizing transparency and trust. Organizations adopting AI systems should prioritize explainability and continuous monitoring to ensure fairness and effectiveness in decision-making processes.