Master Thesis in partnership with YAGHMA 

How can organisations systematically assess and prioritise AI-related risks in a way that is both methodologically rigorous and practically applicable? While numerous ethical guidelines, regulatory principles, and high-level AI governance frameworks exist, organisations often struggle to translate these into concrete risk assessment processes that support real-world decision-making across the AI lifecycle. This gap highlights the need for a structured taxonomy that characterises AI risks in a consistent, operational, and scalable manner. 

This master thesis, supervised in partnership with YAGHMA B.V., focuses on the development of a practical AI risk assessment taxonomy that supports qualitative AI impact assessments in applied contexts. The taxonomy will classify AI risks across selected dimensions—such as ethical, social, governance, and regulatory risks—while explicitly linking them to organisational contexts and stag...