RISKS OF USING AI TECHNOLOGY IN THE RESEARCH PROCESS OF DOCTORAL STUDENTS IN BUSINESS ADMINISTRATION IN VIET NAM: AN APPLICATION OF THE AHP METHOD

Abstract

In the context of rapid digital transformation, the application of artificial intelligence (AI) in scientific research has created numerous opportunities while also posing significant risks for doctoral students in Business Administration. This study aims to examine and categorize the primary risks associated with AI usage, spanning issues related to data, ethics, technology, and user competence. Employing the Analytic Hierarchy Process (AHP) method, the study collected data from 175 doctoral students at major universities in Vietnam to assess the significance of different risk categories. The findings identify four major risk groups: (1) Data risks (weight: 0.56), encompassing incomplete data, biased data (Mirsch et al.), and data security concerns; (2) Ethical risks (0.26), involving challenges such as transparency, privacy violations, and AI decision-making bias; (3) Technical risks (0.12), including software errors and inaccurate AI models; and (4) Competence risks (0.06), which pertain to a lack of data analysis skills and difficulties in selecting appropriate AI tools. The study highlights the importance of identifying and managing these risks while proposing strategies to mitigate them and enhance AI proficiency among doctoral researchers.