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Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 11 [Article No.:] 565 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-15
Verlag: 
MDPI, Basel
Zusammenfassung: 
Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods. This work presents a modeling procedure that carefully builds models around protected class information in order to make sure that the final machine learning model is independent of protected class status, even in a nonlinear sense. This procedure works for any machine learning method. The procedure was tested on subprime credit card data combined with demographic data by zip code from the US Census. The census data serves as an imperfect proxy for borrower demographics but serves to illustrate the procedure.
Schlagwörter: 
unintended bias
fair lending
multihorizon survival models
machine learning
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