Data curation in academic libraries: navigating the AI labyrinth through the FATE principles

Abstract

Introduction. This study examines how academic libraries address the ethical and operational challenges of data curation in an era shaped by artificial intelligence (AI). It focuses on how the FATE principles (fairness, accountability, transparency, and ethics) are interpreted and embedded within AI-driven curatorial practices. Method. A systematic literature review was conducted, analysing fifty-one publications. The review reflexively applied the FATE principles to its own design to ensure methodological integrity, transparency, and reproducibility. Analysis. Both conceptual and empirical dimensions were examined to identify how each FATE principle is operationalised across AI-enabled data workflows. Comparative analysis assessed the procedural maturity of these principles throughout the data lifecycle. Results. The findings reveal a marked imbalance: accountability and transparency demonstrate procedural consolidation through documentation and audit mechanisms, whereas fairness and ethics remain conceptually diffuse and empirically underdeveloped. Academic libraries emerge as ethical and algorithmic infrastructures mediating between technological innovation and epistemic justice. Conclusion. Responsible AI in librarianship requires structural integration of FATE into governance, documentation, and daily curatorial workflows. The proposed FATE Curation Framework embeds ethical checkpoints across the data lifecycle, offering a replicable model for institutional implementation.