Artificial Intelligence ethical frameworks within organizations: Empirical validation of an instrument

This study empirically validates an instrument to assess users' concerns regarding ethical frameworks for Artificial Intelligence (AI) within organizations. The instrument was originally conceptualized with 12 constructs: data responsibility, accountability, data privacy, fairness, explainability, transparency, robustness, moral agency, value alignment, technology misuse, leadership, and trust. The research examines the reliability and interpretability of these constructs relative to their associated variables. Data from 244 subjects across various organizations in the USA were analyzed using Exploratory Factor Analysis (EFA) in SPSS (Version 28). After removing items with significant cross-loadings, the final factor solution was established. The EFA revealed that although 12 theoretical constructs were proposed, users perceive AI ethical concerns through a consolidated 8-factor structure. Key mergers occurred between Data Responsibility and Accountability (Component 1), Robustness and Technology Misuse (Component 2), and Explainability and Transparency (Component 4). Conversely, Data Privacy, Fairness, Leadership, and Trust emerged as distinct, standalone pillars with high internal consistency. The results suggest a high degree of conceptual overlap among stakeholders, with technical implementation and organizational oversight viewed as integrated dimensions. This study offers a streamlined, validated roadmap for organizations to evaluate their ethical standing. It emphasizes that ethical AI adoption requires more than technical robustness; it also needs a cultural shift focused on leadership, strategic vision, and trust.