Countering racial discrimination in algorithmic lending: A case for model-agnostic interpretation methods

Shivam Agarwal, Cal B. Muckley and Parvati Neelakantan

Economics Letters, 2023, vol. 226, issue C

Abstract: In respect to racial discrimination in lending, we introduce global Shapley value and Shapley–Lorenz explainable AI methods to attain algorithmic justice. Using 157,269 loan applications during 2017 in New York, we confirm that these methods, consistent with the parameters of a logistic regression model, reveal prima facie evidence of racial discrimination. We show, critically, that these explainable AI methods can enable a financial institution to select an opaque creditworthiness model which blends out-of-sample performance with ethical considerations.

Keywords: Big-data lending; Machine learning; Algorithmic injustice; Model-agnostic global interpretation methods (search for similar items in EconPapers)
JEL-codes: C52 C55 C58 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:226:y:2023:i:c:s0165176523001428

DOI: 10.1016/j.econlet.2023.111117

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