Identification and Prioritization of Challenges and Solutions for Artificial Intelligence Governance in Management from an Islamic Perspective

Abstract

Objective: This study aimed to identify and prioritize the challenges and solutions of artificial intelligence governance in management from an Islamic perspective, with emphasis on ethical, jurisprudential, legal, institutional, managerial, data-related, and technological dimensions.

Methodology: This applied study employed an exploratory sequential mixed-method design. The study population consisted of academic and professional experts based in Tehran in the fields of management and governance, artificial intelligence and information technology, public policy, technology ethics, and Islamic jurisprudence and law. A total of 24 experts were selected through purposive and snowball sampling. Data were collected using semi-structured interviews, a two-round Delphi questionnaire, and a pairwise comparison questionnaire. Qualitative data were analyzed through thematic analysis. Mean, median, interquartile range, and Kendall’s coefficient of concordance were used to validate the identified components, while the fuzzy analytic hierarchy process was applied to prioritize the final challenges and solutions.

Findings: Kendall’s coefficient of concordance for challenges increased from 0.462 in the first Delphi round to 0.681 in the second round, while the corresponding coefficient for solutions increased from 0.487 to 0.714. The overall second-round coefficient was 0.697 and was statistically significant at p<0.001. Fuzzy prioritization showed that conflict between algorithmic decisions and justice, human dignity, and Islamic values ranked first among the challenges with a weight of 0.164, followed by lack of clear accountability with 0.151 and lack of transparency and explainability with 0.138. Among the solutions, developing a comprehensive Islamic-ethical governance framework ranked first with a weight of 0.171, followed by human-centered accountability mechanisms with 0.156 and algorithmic auditing with 0.141. Sensitivity analysis confirmed the relative stability of the main priority rankings.

Conclusion: Desirable AI governance in management from an Islamic perspective requires the integration of normative logic based on justice and human dignity, institutional logic based on accountability and human oversight, and technical logic based on data quality, bias control, security, and explainability; therefore, managerial use of AI should be implemented through a human-centered, value-oriented, and auditable governance framework.