Big Data Governance Framework for Trustworthy Artificial Intelligence Decision Systems

The rapid adoption of Artificial Intelligence (AI) decision systems has increased organizational dependence on large-scale data, making big data governance a critical requirement for ensuring reliable and responsible decision-making. Although AI systems are often evaluated based on predictive accuracy and computational performance, their trustworthiness is strongly influenced by the quality, security, privacy, traceability, and fairness of the data used throughout the AI lifecycle. This study aims to develop a Big Data Governance Framework for Trustworthy AI Decision Systems by integrating key governance dimensions with trustworthy AI requirements. A qualitative conceptual framework development approach was employed, supported by structured literature review, thematic synthesis, and design science research principles. Relevant literature on big data governance, trustworthy AI, data quality, privacy, security, explainability, accountability, fairness, and AI decision systems was reviewed to identify recurring concepts and research gaps. The results show that trustworthy AI decision systems require seven core governance dimensions: data quality governance, security and privacy governance, metadata and data lineage, bias and fairness control, explainability support, accountability mechanisms, and continuous monitoring. These dimensions strengthen trustworthy AI capabilities, including reliability, transparency, explainability, fairness, privacy preservation, security, robustness, and auditability. The proposed framework demonstrates that trustworthy AI is not only determined by algorithmic performance but also by strong data governance across the AI lifecycle. This study concludes that effective big data governance can improve decision accuracy, traceability, accountability, risk reduction, and stakeholder trust in AI-based decision systems.