From Complexity to Clarity: Enabling Transparent and Responsible AI-Enabled Data Use at WUR Library

At Wageningen University & Research (WUR) Library, we are reimagining how data-driven tools and AI can empower librarians, researchers, educators, and students by making complexity approachable. Our work responds to a fast growing change in academia: how can we engage with powerful AI technologies without compromising transparency, trust, or institutional control over sensitive data? We present a portfolio of initiatives that translate this principle into practice. First, we explore “Talk to Your Data” prototypes that allow users to interact with research output metadata using natural language. This approach demonstrates how generative AI can unlock data accessibility—without transferring any data to external models. In this context, we share our implementation of a privacy-preserving architecture where models are brought to the data, enabling question-answering and summarization tasks while safeguarding compliance with European data requirements. A second focus is our investment in intelligent data quality pipelines. These combine automated validation, enrichment, and feedback loops to reduce manual metadata management. The result: improved metadata reuse across existing and new usecases without an exponential growth in cost. The third layer focuses on advanced analytics use cases powered by shared, well-curated data products. These support research evaluation and strategic decision-making across departments: from standard bibliometric analyses to Standard Evaluation Protocol (SEP)-aligned reporting, with additional usecases on our roadmap centered around the estimation of ROI on research infrastructure and generating trust indicators to help researchers navigate the publishing landscape responsibly. Our approach is grounded in the academic values of openness, reproducibility, and user empowerment. We design with non-technical users in mind, ensuring that tools support explainability, human oversight, and multilingual usability. Through this presentation, we aim to spark conversation on how libraries can lead in embedding AI in ethical, accessible, and human centered ways as well as discuss the challenges and successes, both technical and human, we have learned from along the way.