Preventing Bias in Machine Learning Models of Credit Risk

Dear Colleagues,

The greatest obstacle to widespread adoption of machine learning models in credit risk modelling and loan underwriting is the risk of unintended ethical bias. This is a case of asking the model to do what humans and regulations expect, not what the data reflects. Researchers are exploring ways to modify the data, constrain the algorithms, or alter the modeling process to eliminate these unwanted biases.

For this Special Issue, we invite researchers with novel work into any of these approaches to eliminate bias in the application of machine learning to loan credit risk modeling to submit their papers for consideration. These issues are critical in regulated environments such as lending, but also arise in almost any area where machine learning is applied to human behavior.

Dr. Joseph Breeden