What Drives Racial and Ethnic Differences in High-Cost Mortgages? The Role of High-Risk Lenders

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Predictably Unequal? The Effects of Machine Learning on Credit Markets (2020, methodology): “Our focus in this paper is on the distributional impacts of changes in technology rather than on explicit taste-based discrimination (Becker, 1971) or “redlining,” which seeks to use geographical information to indirectly differentiate on the basis of excluded characteristics, and which is also expl”
Fair lending needs explainable models for responsible recommendation (2018, background): “Accounting for such data quality errors has added moral and ethical dimensions [7, 12], particularly when noting that the presence of such issues is itself correlated with protected classes, such as race, due to complex socioeconomic factors [2, 27, 28, 46].”
Consumer-lending discrimination in the FinTech Era (2019, unknown): “Bayer, Ferreira, and Ross (2018) find that after conditioning on credit characteristics, African American and Hispanic borrowers were 103% and 78% more likely, respectively, than other borrowers to be in a high-cost mortgage between 2004 and 2007.”