Understanding Data Management for Ethical and Transparent AI

This presentation discusses the role of research data management (RDM) as a foundational element of responsible and sustainable scientific research. Rather than treating data management as a compliance requirement, the talk highlights its strategic importance for ensuring scientific validity, ethical integrity, and legal accountability in data-driven research. Using examples from health AI and public data governance, the presentation illustrates how weaknesses in data governance—such as limited representativeness of datasets, fragmented consent practices, and poor provenance tracking—can lead to significant scientific and ethical risks. It further reflects on the challenges posed by multinational research environments, where legal frameworks, ethical standards, and technical infrastructures intersect. The presentation emphasizes the importance of structured data governance practices, including FAIR data principles and well-designed Data Management Plans (DMPs), to support reproducibility, responsible data use, and long-term research value.