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De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data

Nicholas Tenev

arXiv 1 May 2024 · Machine Learning · 1 citations (OpenAlex)

arXiv:2405.00910 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Prediction models can improve efficiency by automating decisions such as the approval of loan applications. However, they may inherit bias against protected groups from the data they are trained on. This paper adds counterfactual (simulated) ethnic bias to real data on mortgage application decisions, and shows that this bias is replicated by a machine learning model (XGBoost) even when ethnicity is not used as a predictive variable. Next, several other de-biasing methods are compared: averaging over prohibited variables, taking the most favorable prediction over prohibited variables (a novel method), and jointly minimizing errors as well as the association between predictions and prohibited variables. De-biasing can recover some of the original decisions, but the results are sensitive to whether the bias is effected through a proxy.

Citation extraction

38
references
46
in-text mentions
38
distinct cited
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7,461
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appendix boundary found by appendix_titled_section at “Appendix” · 98% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Pope, D. G.\ \ Sydnor, J. R Implementing anti-discrimination policies in statistical profiling models\1.00053100%
2Ravichandran, S., Khurana, D., Venkatesh, B., \ Edakunni, N. U FairXGBoost: Fairness-aware classification in XGBoost\0.73732100%
3Derenoncourt, E., Kim, C. H., Kuhn, M., \ Schularick, M (1860) The racial wealth gap, 1860-2020\0.64422100%
4Chen, I., Johansson, F. D., \ Sontag, D Why is my classifier discriminatory?\0.51121100%
5Ladd, H. F Evidence on discrimination in mortgage lending\0.40511100%
6OCC Comptroller's Handbook: Fair Lending0.40511100%
7Aaronson, D., Hartley, D. H., \ Mazumder, B (1930) The effects of the 1930s HOLC redlining maps\0.40511100%
8Akbar, P. A., Hickly, S. L., Shertzer, A., \ Walsh, R. P Racial segregation in housing markets and the erosion of black wealth\0.40511100%
9Bartlett, R., Morse, A., Stanton, R., \ Wallace, N Consumer-lending discrimination in the FinTech era\0.40511100%
10Bertrand, M.\ \ Mullainathan, S Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination\0.40511100%

Showing the top 10 of 38 scored citations.