The Mathematical Validation of AI
A Case Study on Bias Reduction in the Fraud Risk Model of the Municipality of Rotterdam
C.J. Versluis (TU Delft - Electrical Engineering, Mathematics and Computer Science)
V.N.S.R. Dwarka – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
M.E. Kootte – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
G.F. Nane – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
This study critically examines the fairness of Rotterdam’s fraud detection system using the Lighthouse Reports’ Suspicion Machine framework. By replicating and extending the original model with gradient boosting, dimension- ality reduction (PCA), clustering, and adversarial debiasing, the analysis highlights how small changes in input or weighting can substantially alter bias across predefined archetypes. Although clustering revealed no clear separa- tion in risk scores, weighting and adversarial techniques reduced disparities between groups. Limitations include the synthetic nature of the dataset, lack of real fraud labels, and restricted focus on 13 archetypes. The findings stress the importance of validating input data and model design, as fairness outcomes remain highly sensitive to methodological choices.