This paper uses various types of machine learning models to predict credit risk using historical mortgage data. It finds gains in predictiveness that would likely lead to an increase in approvals across all demographic groups, but that average prices could increase for African-American and Hispanic borrowers due to differences in risk calculations.

This paper examines AI systems and how these systems should be held accountable, in particular by one method: explanation. The paper focuses on using explanation from AI systems at the right time to improve accountability, and reviews societal, moral, and legal norms around explanation. The paper ends with advocating that at present, AI systems can and should be held responsible to a similar standard of explanation as humans are, and adapt as the future changes.

This paper evaluates differentials between borrowers of different races in loan approval rates and pricing between fintech and traditional mortgage lenders. The paper finds that unexplained pricing differentials are smaller among technology-heavy lenders and that such differentials overall have declined as the mortgage industry as increased reliance on algorithmic lending in recent years. UC Berkeley Public Law Research Paper

The paper evaluates the impact of aspects of customer-bank relationships on loan default rates. Using a dataset that consists of more than 1 million loans made by 296 German banks, the research finds that banks with relationship-specific information from customers establishing transaction accounts are less likely to experience loan defaults and act differently in screening and monitoring behaviors than banks with no information.

This paper examines concerns about big data’s disparate impact risk from the perspective of American antidiscrimination law, more specifically, through Title VII’s prohibition of discrimination in employment. The paper also calls out the legal and political difficulties of addressing and remedying this type of discrimination, in particular, the tension between the two major theories underlying antidiscrimination law: anticlassification and antisubordination.