Assessing Fair Lending Risks Using Race/Ethnicity Proxies

By Yan Zhang. 2018

78 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. Jiahao Chen
2. Madeleine Udell
3. Nathan Kallus
4. Geoffry Svacha
5. Xiaojie Mao

In Economics and Econometrics

1. Jiahao Chen
2. Madeleine Udell
3. Nathan Kallus
4. Geoffry Svacha
5. Xiaojie Mao

Citations in the corpus

• Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved (2018, methodology): “for which the geolocation (state, county, and census tract), self-reported race/ethnicity, and loan origination outcome were reported, and has been used in the literature to evaluate the BISG race proxy [1, 12, 31].”
• Fair lending needs explainable models for responsible recommendation (2018, background): “However, the resulting assessment seems to overestimate the amount of disparate impact [4, 60] and has resulted in some controversy [4, 14–16, 19, 33].”