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

Author

Listed:
  • Agarwal, Shivam
  • Muckley, Cal B.
  • Neelakantan, Parvati

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.

Suggested Citation

  • Agarwal, Shivam & Muckley, Cal B. & Neelakantan, Parvati, 2023. "Countering racial discrimination in algorithmic lending: A case for model-agnostic interpretation methods," Economics Letters, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:ecolet:v:226:y:2023:i:c:s0165176523001428
    DOI: 10.1016/j.econlet.2023.111117
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    References listed on IDEAS

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    1. Paolo Giudici & Emanuela Raffinetti, 2020. "Lorenz Model Selection," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 754-768, October.
    2. Bartlett, Robert & Morse, Adair & Stanton, Richard & Wallace, Nancy, 2022. "Consumer-lending discrimination in the FinTech Era," Journal of Financial Economics, Elsevier, vol. 143(1), pages 30-56.
    3. de Andrés, Pablo & Gimeno, Ricardo & Mateos de Cabo, Ruth, 2021. "The gender gap in bank credit access," Journal of Corporate Finance, Elsevier, vol. 71(C).
    4. Patrick Bayer & Fernando Ferreira & Stephen L. Ross, 2018. "What Drives Racial and Ethnic Differences in High-Cost Mortgages? The Role of High-Risk Lenders," Review of Financial Studies, Society for Financial Studies, vol. 31(1), pages 175-205.
    5. Brent W Ambrose & James N Conklin & Luis A Lopez, 2021. "Does Borrower and Broker Race Affect the Cost of Mortgage Credit? [Why don’t lenders renegotiate more home mortgages? Redefaults, self-cures and securitization]," Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 790-826.
    6. Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Big-data lending; Machine learning; Algorithmic injustice; Model-agnostic global interpretation methods;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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