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One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas
Machine learning (ML) models can help increase access to credit in lower-income areas if their introduction is paired with "fairness constraints," which are conceptually similar to the familiar Special Purpose Credit Programs (SPCP). Doing this at scale would require rethinking the protected attribute blindness requirements of the policy.
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Authors:
Vitaly Meursault, Daniel Moulton, Larry Santucci, Nathan Schor
Status:
Accepted at JPAM
Updated:
May 2024
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Operationalizing the Search for Less Discriminatory Alternatives in Fair Lending
The less discriminatory alternative (LDA) is a legal key provision for the US fair lending law. It requires lenders to adopt models that reduce disparate impact when they do not compromise their business interests. Systematically searching for such LDA models is quite challenging, however. Here, we show how a complex mixed integer programming algorithm allows us to set up the problem in a direct and intuitive way.
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Authors:
Talia Gillis, Vitaly Meursault, Berk Ustun
Status:
Published at FAccT (2024)
Updated:
Jun 2024