Towards Explaining Automated Credit Decisions: The design of an Explicability Assessment Framework (EAF) for Machine Learning Systems

The use of machine learning systems has great potential to better predict probabilities of default for credit underwriting. Despite this advantage, herewith there exists the substantial risk of discrimination. Moreover, machine learning models with the highest prediction-accuracy are often the least explicable (i.e. explainable). Nonetheless,...