Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles

Background: Data quality is a cornerstone of scientific integrity, reproducibility, and deci-sion-making. However, datasets often lack transparency in their collection and curation processes. Methods: We conducted a narrative review of the scientific and technical litera-ture published between 1996 and 2025, complemented with standards (e.g., ISO/IEC 25012, ISO 8000) and reports addressing data quality frameworks. Sources were retrieved from PubMed, Scopus, Web of Science, and grey literature. The review identifies core di-mensions, practical applications, and challenges in data quality management. Results: Across sectors, accuracy, completeness, consistency, timeliness, and accessibility emerged as universal dimensions of quality. Healthcare and business provide illustrative case studies where poor data quality leads to significant clinical and economic risks. Recent frameworks integrate data governance, FAIR (findability, accessibility, interoperability, and reusability) principles, and ethical considerations, including transparency and bias reduction in artificial intelligence. Conclusions: Data quality presents both technical and socio-organizational challenges. Embedding quality assurance into the full data lifecycle and aligning with FAIR and governance frameworks is essential for trustworthy, reusable datasets. This review provides a structured synthesis that can inform research, policy, and practice in managing high-quality data.