Perceived AI Consumer-Driven Decision Integrity: Assessing Mediating Effect of Cognitive Load and Response Bias
Syed Md Faisal Ali Khan, Yasser Moustafa Shehawy
Abstract
This study examines the influence of artificial intelligence (AI) system transparency, cognitive load, response bias, and individual values on perceived AI decision integrity. Using a quantitative approach, data were collected through surveys and analyzed via SEM-PLS. The findings highlight that AI transparency and familiarity significantly impact users’ trust and perception of decision fairness. Response biases were found to be increased by the cognitive load and decision fatigue, affecting decision integrity. This study identifies mediating effects of sensitivity to errors and response bias in AI-driven decision-making. Practical implications imply that lowering the cognitive load and increasing transparency will help to increase the acceptance of AI, and incorporating ethical considerations into AI system design helps to minimize bias. This study contributes to AI ethics by emphasizing fairness, explainability, and user-centered trust mechanisms. Future research should explore AI decision-making across industries and cultural contexts. The findings of this study offer managerial, theoretical, and practical insights into responsible AI deployment.