Trust and Accountability in Knowledge Graph-Based AI for Self Determination

Abstract

This report documents the program and results of the Dagstuhl Seminar 25051 "Trust and Accountability in Knowledge Graph-Based AI for Self Determination". The seminar focused on AI systems powered by Knowledge Graphs and their fundamental role in powering intelligent decision making. Knowledge Graphs complement Machine Learning algorithms by providing data context and semantics, enabling further inference and question answering capabilities, and their synergy with Large Language Models is being actively researched. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may raise the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destiny. To counter this threat, AI regulation, such as the EU AI Act, is being proposed in certain regions. Regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fueling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making?