Unraveling the Impact of Ethical Generative Ai on Organizational Performance: A Hybrid Causal and Predictive Analysis Using Sem and Machine Learning

Abstract

The rise of Generative Artificial Intelligence (GenAI) has revolutionized business operations, enhancing productivity and innovation. However, ethical concerns regarding fairness, accountability, transparency, accuracy, and autonomy (FATAA principles) challenge responsible AI adoption. Accordingly, this study examines the impact of ethical GenAI on organizational performance, integrating Structural Equation Modeling (SEM), Machine Learning (ML), and Chord diagrams visualizations. It explores how FATAA principles influence GenAI adoption and whether ethical leadership moderates its impact on organizational performance. A cross-sectional survey of 301 AI-literate professionals across industries was analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to test hypotheses, while Logistic Regression, Random Forest and Decision Tree models provided predictive insights. Results indicate that fairness and accuracy significantly affect GenAI adoption, whereas accountability, transparency, and autonomy have weaker influences. Moreover, ethical leadership does not significantly moderate the relationship between GenAI usage and performance, suggesting AI-driven success depends more on operational efficiencies rather than leadership values. Machine learning analysis highlights random forest models as superior in predicting AI adoption patterns, emphasizing data-driven governance. Chord diagram visualizations illustrate AI governance interdependencies, offering critical insights for policymakers and business leaders. This study contributes to AI ethics research by combining causal inference and predictive modeling for responsible AI deployment.

Keywords: Generative AI, Ethical AI, Organizational Performance, Machine Learning, Structural Equation Modeling, Ethical Leadership, Chord diagram visualization