Category: Fair Lending

Reducing the Black-White Homeownership Gap through Underwriting Innovations

Read Report

This study updates mortgage market developments in the use of cash-flow information from bank accounts and utility, telecommunications, and rental payments history. The report highlights issues concerning data collection, standardization, and consumer protection regulation when using non-traditional financial data sources, as well as the impact of pricing, servicing, and regulation in determining whether the use of such data sources enhances racial equity.

Jung Hyun Choi et al., Urban Institute

Lessons Learned from Mortgage Borrower Policies and Outcomes during the COVID-19 Pandemic

Read Study

This study analyzes the aggregate impacts of mortgage forbearances and the Federal Reserve’s large-scale asset purchase program through Q3 2021 with a particular eye toward effects on different racial and ethnic groups.

Kristopher Gerardi, Lauren Lambie-Hanson, and Paul Willen; Federal Reserve Bank of Boston Current Policy Perspectives

Affirmative Algorithms: The Legal Grounds for Fairness as Awareness

Read Paper

The authors explore the application of modern antidiscrimination law to algorithmic fairness techniques and find incompatibility between those approaches and equal protection jurisprudence that demands “individualized consideration” and bars formal, quantitative weights for race regardless of purpose. The authors look to government-contracting cases as an alternative grounding for algorithmic fairness, because these cases permit explicit and quantitative race-based remedies based on historical discrimination by the actor. But while limited, this doctrinal approach mandates that adjustments be calibrated to the entity’s responsibility for historical discrimination causing present-day disparities. The authors argue that these cases provide a legally viable path for algorithmic fairness under current constitutional doctrine but call for more research at the intersection of algorithmic fairness and causal inference to ensure that bias mitigation is tailored to specific causes and mechanisms of bias.

Daniel E. Ho and Alice Xiang, University of Chicago Law Review

Big Data and Discrimination

Read Paper

This essay discusses the legal requirements of pricing credit and the architecture of machine learning and intelligent algorithms to provide an overview of legislative gaps, legal solutions, and a framework for testing discrimination that evaluates algorithmic pricing rules. Using real-world mortgage data, the authors find that restricting the data characteristics within the algorithm can increase pricing gaps while having a limited impact on disparity.

Talia B. Gillis & Jann L. Spiess, University of Chicago Law Review

Big Data’s Disparate Impact

Read Paper

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.

104 California Law Review 671 (2016)

Solon Barocas, Andrew D. Selbst

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Read Paper

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.

Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, and Ansgar Walther.

Consumer Lending Discrimination in the FinTech Era

Read Paper

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

Robert Bartlett, Adair Morse, Richard Stanton, Nancy Wallace.

Join our Newsletter

Translate »