Algoprudence repository
Stakeholders learn from our case law for algorithms (algoprudence), can help to improve it and can use it to resolve ethical issues in a harmonized manner when deploying algorithmic systems
Case repository
Predicting irresponsible driving
Case study how irresponsible driving can be identified and predicted in the database of a car sharing platform. An independent commission issues advice on among others model validity, balancing false positives and false negatives and meaningful transparency. Read more...
Addendum Preventing prejudice
Further research into CUB process of Education Executive Agency of The Netherlands (DUO) by analysing aggregation statistics on the country of birth and country of origin of 300.000+ students in the period 2014-2022 provided by the Dutch national office of statistics Read more...
Preventing prejudice
Disparities have been identified in the control process of a Dutch public sector organisation regarding misuse of college allowances. In the period 2012-2022, students who lived close to their parent(s) were significantly more often selected for a control procedure than others. The algorithm used to support the selection performed as expected. Read more...
Risk Profiling for Social Welfare Reexamination
Case study to ML-driven risk predictions on unduly granted social welfare. An independent commission judges that algorithmic risk profiling can be used under strict conditions for sampling residents for re-examination. The aim of re-examination is a leading factor in judging profiling criteria. Read more...
BERT-based disinformation classifier
Case study to algorithmic detection on fake news on Twitter. An independent advice commission believes there is a low risk of proxy discrimination by the BERT-based disinformation classifier and that the particular difference in treatment identified by the quantitative bias scan can be justified, if certain conditions apply. Read more...
Type of SIM card as a predictor variable to detect payment fraud
Case study to ML-driven risk profiling to detect after-pay fraud at an e-commerce platform. An independent commission advises against using type of SIM card as an input variable in the algorithmic risk model. As it is likely that type of SIM card acts as a proxy-variable for sensitive demographic categories, the model would run an intolerable risk of disproportionally excluding vulnerable demographic groups from the payment service. Read more...
