An Empirical Analysis of Consumer Risk Perceptions in Artificial Intelligence (AI) Applications: AI-Driven Information Governance (AIG)

Abstract

a {  text-decoration: none;  color: #464feb; } tr th, tr td {  border: 1px solid #e6e6e6; } tr th {  background-color: #f5f5f5; } The growing integration of artificial intelligence (AI) into consumer products has raised significant concerns regarding privacy, security, and user control. Despite these concerns, limited research has explored how consumers perceive and evaluate the risks associated with AI systems. This study examines three key AI-related risks: informational intrusion, cyber exposure, and loss of control. It argues that these risks are largely driven by a central factor, algorithmic opacity, which refers to the lack of transparency and the difficulty consumers face in understanding how AI systems operate. Data for this study were collected through online communities, including the Pakistan Students and Scholars Association South Korea, which has approximately 8,000 followers, and Seekers, a diverse network comprising students, professionals, businesspeople, laborers, artists, and other community members, with approximately 17,000 followers across both platforms. Using a structured questionnaire, a total of 300 valid responses were gathered and analyzed using SPSS and AMOS 26. The findings reveal that all three identified risks are strongly associated with perceptions of algorithmic opacity. In turn, perceived opacity is significantly linked to feelings of unfairness and discomfort among consumers. By contrast, the three risks exert only weak direct effects on these outcomes, suggesting that consumers respond more strongly to the transparency of AI systems than to the risks themselves. Overall, the results indicate that the relationship between governance-related AI risks and negative consumer responses is largely mediated by algorithmic opacity. In other words, a lack of transparency serves as the primary mechanism through which AI-related risks influence perceptions of unfairness and discomfort. From a practical perspective, the findings highlight the importance of improving transparency in AI systems. Organizations can mitigate negative consumer perceptions by providing clearer explanations of AI-driven decisions and offering users greater control over AI-enabled processes. Enhancing transparency and understanding can increase perceptions of fairness and comfort, ultimately strengthening consumer trust in AI systems. Therefore, improving the transparency, explainability, and user comprehension of AI technologies should be a key priority for organizations seeking to foster greater acceptance and trust in AI-driven products and services.