Master Thesis in partnership with YAGHMA 

How can organisations effectively mitigate risks related to bias and lack of transparency in AI systems in real-world applications? While bias and transparency are widely recognised as critical ethical and societal risks of AI, organisations often struggle to move beyond high-level principles toward concrete mitigation measures that can be applied, evaluated, and monitored in practice. This challenge underscores the need for structured approaches that connect risk identification with actionable mitigation strategies across the AI lifecycle. 

This master thesis, supervised in partnership with YAGHMA B.V., focuses on researching, selecting, and applying mitigation measures aimed at reducing bias and improving transparency in AI systems. The thesis combines conceptual analysis with applied case studies, linking AI risk assessment to concrete organisational interventions within one or more illustrative AI use cases.