Author: Tamara K. Nopper; Publisher: Medium; Publication Year: 2022. The following article examines the possibilities of alternative data initiatives for mitigating racial inequality in the U.S. credit scoring system This discusses the for-profit credit evaluation system and how alternative data and fundamental shifts in the system that performs credit evaluation can improve the experience for minority users…
Author: Sonja Kelly, Mehrdad Mirpourian; Publisher: Women’s World Banking; Publication Year: 2021. The following report discussed where gender-based bias originates and how to mitigate such biases in the emerging digital credit space. The report discussed the data collection process on emerging credit platforms and what data would be collected. It also introduced 3 potential origins of bias in algorithms, including sampling bias, labeling bias…
Author: Kevin Peachey; Publisher: BBC News; Publication Year: 2019. The following article describes how in November 2019, a tech entrepreneur reported that he was approved credit 20 times more on his Apple Card than his wife, even though they have equal shares in their property and file joint tax returns, and she even had a better credit score than him. Regulators began to investigate and Goldmann Sachs, the bank…
Author: Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan; Publisher: N/A; Publication Year: 2016. The following publication discusses issues around risk scoring with credit scores and attempts to define “fairness” in this context. The main idea of this journal article is that there are various notions of what is fair when it comes to risk scoring. To address this, the authors proposed 3 conditions that should be met when considering algorithmic fairness…
Author: Genevieve Smith, Ishita Rustagi; Publisher: Stanford Social Innovation Review; Publication Year: 2021. The following article discusses the effect of gender bias in data science. A very interesting example is the credit lines and credit scores given out to men and women with very similar profiles. In one case, a husband and wife compared their Apple credit lines only to find out the man had a credit line of 20 times that of his wife. The authors note that…
Author: Edmund L. Andrews; Publisher: Stanford University; Publication Year: 2021. The following article discusses how artificial intelligence (AI) models used to determine credit worthiness for lending are less accurate for lower-income and minority borrowers, in part due to less information on their credit reports. This leads to fewer loans to these borrowers and perpetuates the problem that there is little information about…
Author: Denny Lee, Brooke Wenig, Diana Pfeil; Publisher: Data Brew; Publication Year: N/A. The following video series discusses 3 large topics – transparency, privacy, and bias. First was the idea of transparency in data science – one example is that of a credit score. If you are rejected for a loan, you have a right to know why you were rejected and more specifically what factors and weights impacted that rejection. The privacy concerns brought…