Agarwal, Shivam and Muckley, Cal B. and Neelakantan, Parvati
(2023)
Countering racial discrimination in algorithmic lending: A case for model-agnostic interpretation methods.
Economics Letters, 226.
p. 111117.
ISSN 01651765
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.
| Item Type: |
Article
|
| Keywords: |
Big-data lending;
Machine learning;
Algorithmic injustice;
Model-agnostic global interpretation;
methods; |
| Academic Unit: |
Faculty of Social Sciences > School of Business |
| Item ID: |
17130 |
| Identification Number: |
https://doi.org/10.1016/j.econlet.2023.111117 |
| Depositing User: |
Shivam Agarwal
|
| Date Deposited: |
02 May 2023 10:20 |
| Journal or Publication Title: |
Economics Letters |
| Publisher: |
Elsevier |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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