Preface for the 2nd International Workshop on Knowledge Graphs for Responsible AI (KG-STAR 2025)

Abstract

Responsible AI hinges on formalizing fairness, transparency, accountability, and inclusivity throughout the AI lifecycle–a need that grows ever more urgent as generative models scale in capability and complexity. Knowledge Graphs (KGs) offer a structured semantic backbone that enriches generative AI by injecting contextual priors, elucidating model inferences, and curbing bias propagation. By encoding entities and relations, KGs enable interpretable reasoning paths–allowing practitioners to audit decision logic–and unify diverse data sources to ensure comprehensive, equitable coverage. This semantic scaffolding thus underpins responsible AI by making generative outputs more explainable, traceable, and aligned with ethical safeguards. The 2nd International Workshop on Knowledge Graphs for Responsible AI (KG-STAR 2025) focused on the role of KGs in promoting Responsible AI principles and creating a cooperative space for researchers, practitioners, and policymakers to exchange insights and enhance their understanding of KGs’ impact on achieving Responsible AI solutions. It aimed to facilitate collaboration and idea-sharing to advance the understanding of how KGs can contribute to Responsible AI. The workshop featured two thought-provoking keynote talks and four insightful research presentations exploring the intersection of Knowledge Graphs and Responsible AI.