Artificial Intelligence (AI) has rapidly evolved into a transformative force that significantly influences daily human decision-making across personal, social, and professional contexts. Unlike traditional computational systems, modern AI technologies are capable of learning from data, adapting to user behavior, and generating predictive outputs that shape choices in real time. In digital environments such as e-commerce platforms, navigation systems, streaming services, and virtual assistants, AI filters information, prioritizes alternatives, and presents structured recommendations that guide user behavior. Amershi et al. (2019) [1] emphasize that AI systems are increasingly functioning as collaborative partners rather than passive tools, fundamentally altering how individuals interact with technology. As a result, the decision-making process has shifted from being entirely human-driven to a hybrid cognitive model where algorithmic intelligence actively participates in shaping judgments and outcomes. The growing integration of AI into economic and social systems has restructured how information is accessed and processed. Brynjolfsson and McAfee (2017) [2] argue that AI-powered digital platforms enhance efficiency by reducing search costs and accelerating information flow, thereby enabling faster and more data-informed decisions. Through predictive analytics and personalization algorithms, AI anticipates user preferences and delivers customized options tailored to individual needs. While such personalization increases convenience and engagement, it also narrows exposure to diverse alternatives, potentially reinforcing existing preferences and behavioral patterns. Consequently, AI does not merely assist in decision-making; it subtly modifies the architecture of choice by shaping what options are visible and prioritized. Transparency and explainability represent critical challenges in AI-mediated decision-making environments. As AI systems become more complex, particularly with the adoption of deep learning models, understanding how decisions are generated becomes increasingly difficult for end users. Doshi-Velez and Kim (2017) [3] stress the necessity of interpretable machine learning frameworks to ensure accountability and maintain user trust. When individuals cannot understand the reasoning behind AI-generated outputs, they may either over-trust or under-trust the system. Effective explainability mechanisms are therefore essential to support informed reliance, allowing users to evaluate recommendations critically rather than accepting them blindly. Another important aspect of AI’s impact on daily decision-making involves automation and human reliance. As AI systems move from advisory roles to autonomous agents capable of executing tasks, users increasingly delegate responsibility to algorithms. Parasuraman and Riley (1997) [4] warn that automation can lead to misuse, disuse, or abuse when human oversight diminishes. In contexts such as healthcare diagnostics, financial investment management, and transportation systems, excessive reliance on automated outputs may reduce human vigilance and critical thinking. This phenomenon highlights the importance of calibrated trust and the continued involvement of human judgment in high-stakes decisions.
Human–AI Interaction, Artificial Intelligence, Daily Decision-Making, Intelligent Systems, Machine Learning, Recommendation Systems, User Behavior, Human-Centered Design, Automation, Ethical AI, Trust in AI, Transparency, Bias in AI, Decision Support Systems, Digital Assistants, Technology Adoption, Cognitive Support, Responsible AI.
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