Yesterday, with Laurence Barry, we posted a blog post “Who benefits from data sharing?” explaining why data sharing, in insurance, could end mutualization. Actually, it can also be bad in the context of discrimination. Consider here the same dataset, with claim occurence, in a real insurance portfolio, library(InsurFair) library(randomForest) Consider a version of this dataset without the gender, and use variable importance to get a list of variables we can use in a predictive model subfrenchmotor = frenchmotor[,-which(names(frenchmotor)=="sensitive")] RF = randomForest(y~. ,data=subfrenchmotor) vi = … Continue reading Discrimination by proxy (a real case study) →

R-bloggers

The post discusses the potential negative effects of data sharing in insurance, specifically in terms of discrimination. It presents a case study using a dataset without gender information and demonstrates how a gender-blind predictive model still produces discriminatory results.