Leveraging Knowledge Graphs for AI System Auditing and Transparency

Abstract

Auditing complex Artificial Intelligence (AI) systems is gaining importance in light of new regulations and is particularly challenging in terms of system complexity, knowledge integration, and differing transparency needs. Current AI auditing tools however, lack semantic context, resulting in difficulties for auditors in effectively collecting and integrating, but also for analysing and querying audit data. In this position paper, we explore how Knowledge Graphs (KGs) can address these challenges by offering a structured and integrative approach to collecting and transforming audit traces. This work discusses the current limitations in both AI auditing processes and tools. Furthermore, we examine how KGs can play a transformative role in overcoming these obstacles to achieve improved auditability and transparency of AI systems.