Credit Scoring and Its Effects on the Availability and Affordability of Credit

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Fair Regression: Quantitative Definitions and Reduction-based Algorithms (2019, background): “Predictions of these quantities are treated as scores, which are used by human decision makers, perhaps in the context of a partly automated workﬂow, to reach ﬁnal decisions (see, e.g., Waters & Miikkulainen, 2014; US Federal Reserve, 2007; Northpointe, 2010; Lowenkamp et al., 2012).”
Classification situations: Life-chances in the neoliberal era (2013, unknown): “their white counterparts, net of other differences (FRB, 2007).”
Machine Learning Testing: Survey, Landscapes and Horizons (2019, background): “FICO scores [282] United States credit worthiness 301,536 fairness”
Learning Global Additive Explanations for Neural Nets Using Model Distillation (2018, background): “Domains such as credit scoring have regulatory requirements that prescribe monotonic relationships between predictions and some features [13].”
Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination (2019, background): “For example, one of the few, robust public studies on credit scores and discrimination in the United States was performed by the FRB in 2007, at the direction of Congress [56].”