Exploring Explainable AI Transparency in Managerial Decision-Making
Rita El Guermai, Abdelfattah Jamal, Karima Aissaoui · Advances in computational intelligence and robotics book series · 2025
The research investigates how managers perceive AI transparency through accessibility, comprehensibility, and fairness which determines their level of interaction with AI-assisted decision systems. Managerial interaction serves as the outcome variable which this study categorizes into three ordered levels: low, moderate, and high. The research combines explainable AI frameworks (LIME and SHAP) with organizational trust and transparency models to connect cognitive and ethical aspects of human–AI interaction. A structured survey was conducted among 180 managers from three key sectors in Morocco: banking, telecommunications and retail. The questionnaire was developed from validated scales and analyzed by logistic regression models, run in Python for algorithmic flexibility and in SAS to ensure statistical robustness and reproducibility.