Ensuring Data Quality in the AI Era: Insights into Governance, Ethical Considerations, and Fair Principles"

Abstract

The rapid proliferation of Artificial Intelligence (AI) across industries has placed data quality at the forefront of organizational priorities. High-quality data is essential not only for the accurate training of AI models but also for ensuring ethical, transparent, and reliable outcomes. This paper explores the evolving landscape of data quality in the AI era, emphasizing governance frameworks, ethical considerations, and the application of FAIR (Findable, Accessible, Interoperable, and Reusable) principles. It highlights the challenges posed by large, diverse, and rapidly changing datasets, including bias, incompleteness, inconsistency, and lack of traceability. Through analysis of contemporary practices, this study demonstrates how robust data governance structures, metadata management, and standardized quality metrics enhance both the usability and trustworthiness of AI systems. The paper further examines ethical concerns such as fairness, accountability, and transparency, underscoring the critical role of governance in mitigating risks related to biased or misused data. Implementing FAIR principles provides a systematic approach to ensuring data is not only high-quality but also accessible and interoperable across platforms and organizational boundaries, facilitating reproducibility and collaborative innovation. By integrating governance, ethics, and FAIR principles, organizations can create data ecosystems that maximize AI effectiveness while minimizing operational, reputational, and ethical risks. This review provides actionable insights for data practitioners, AI developers, and policymakers aiming to foster reliable, responsible, and high-quality data practices in AI-driven enterprises.