De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data

arXiv:2405.00910v1 Announce Type: new
Abstract: Prediction models can improve efficiency by automating decisions such as the approval of loan applications. However, they may inherit bias against protected groups from the data they are trained on. This paper adds counterfactual (simulated) ethnic bias to real data on mortgage application decisions, and shows that this bias is replicated by a machine learning model (XGBoost) even when ethnicity is not used as a predictive variable. Next, several other de-biasing methods are compared: averaging …

abstract application application data applications arxiv bias comparison counterfactual cs.cy cs.lg data decisions econ.em efficiency however mortgage paper prediction prediction models real data type