As part of the National Police Lab AI (NPAI), I conduct research into making data-driven automated decision making (machine learning) transparent and explainable. This new research area, Explainable Artificial Intelligence (XAI), combines insights from algorithmic decision-making, philosophy, and (social) psychology.
For the Netherlands National Police, I support practitioners in embedding explainability and transparency during the design, development and operation of AI applications. My helps data scientists to gain trust in their AI models, and to be confident in creating and applying AI models in a legally and ethically correct manner. (GCA) depicts the effects of task-, fairness- and robustness-related features on global black-box AI behavior in a single graphical overview—fostering analysis and discussion.
In collaboration with , I assess how (X)AI can meaningfully contribute to decision-making processes in the National Police, while taking into account end-user expectations and ethical principles.
In September 2019 my master's thesis Contrastive Explanation for Machine Learning won the Graduate School of Natural Sciences thesis award, and was runner-up for the 乐鱼后台 Best Master's Thesis 2019.
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