Automated underwriting in mortgage lending: Good news for the underserved?

By S. Gates, V. G. Perry, P. Zorn. 2002

77 citations recorded by the source corpus.

How researchers see this paper

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Diagnosing Gender Bias in Image Recognition Systems (2020, neutral): “The promise of machine-learning algorithms has been that they would bypass this aspect of human bias, leading to more accurate or equitable results (Cowgill 2018; Gates, Perry, and Zorn 2002; Kleinberg, Ludwig et al. 2018).”
The Sociology of Discrimination: Racial Discrimination in Employment, Housing, Credit, and Consumer Markets. (2008, background): “Conversely, Gates et al. (2002) report that the use of automated underwriting systems (removing lender discretion) was associated with a nearly 30% increase in the approval rate for minority and low-income clients and at the same time more accurately predicted default than traditional methods.”