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Saturday, June 10 • 10:40am - 11:00am
Credit Scoring Under Racial Capitalism: Problems of Transparency, Algorithms, and Access

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In the United States, the current primary tool to determine one’s financial health is the credit score. Required for access to loans, lines of credit, mortgages, and higher education, the credit score is a measure of an individual’s credit worthiness. Automated by machine processing and controlled by proprietary algorithms, credit scores are purported to be more neutral, more fair, and to increase equitable access of individuals to lines of credit. But whether these technologies truly offer neutrality and equity is a much more nuanced discussion.

As free credit monitoring through countless online platforms, websites, and applications has risen in popularity in recent years, the topic of credit scoring has only become more visible, though most still guard their credit scores as closely as their Social Security numbers, sharing the exact number with only their most intimate associates, and when required by employers, landlords, and financial entities. Few discussions of credit scores and the models that generate them involve the foundations upon which this technology was built, nor do they contextualize credit scoring within the history of debt and credit in America.

This paper outlines an analysis of credit scores as an information technology through the lens of America’s racialized economic history. Using the structure of racial capitalism (Robinson, 2000), it traces economic developments during the transition from slavery to the era of the Jim Crow laws (Alexander, 2012; Dawson & Francis, 2016), and later through the rise of Neoliberalism (Lazzarato et al., 2014), and identifies a foundation for contemporary credit scoring models that incentivizes the indoctrination of indebted citizens, and exploits portions of the population, especially communities of Color and those living at or below the poverty line (Cohen-Cole, 2011; McClanahan, 2014). It begins with the history of economic predation against Black Americans, and then expands to a broader intersectional view of groups most vulnerable to current exploitative financial practices linked to credit scoring models (O’Neil, 2017; Wang, 2018), especially the more recent development of alternative scoring models, or e-scoring models (Capon, 1982; Hurley & Adebayo, 2017), and ends with an eye towards future protections and regulations that could create a more equitable financial system in America.

Sources Used:
Alexander, M. (2012). The New Jim Crow: Mass Incarceration in the Age of Colorblindness. New Press.
Capon, N. (1982). Credit Scoring Systems: A Critical Analysis. Journal of Marketing, 46(2), 82–91.
Cohen-Cole, E. (2011). Credit Card Redlining. The Review of Economics and Statistics, 93(2), 700–713.
Dawson, M. C., & Francis, M. M. (2016). Black Politics and the Neoliberal Racial Order. Public Culture, 28(1), 23–62.
Hurley, M., & Adebayo, J. (2017). Credit Scoring in the Era of Big Data. Yale Journal of Law and Technology, 18(1).
Lazzarato, M., Charbonneau, M., & Hansen, M. P. (2014). Debt, Neoliberalism and Crisis: Interview with Maurizio Lazzarato on the Indebted Condition. Sociology, 48(5), 1039–1047.
McClanahan, A. (2014). Bad Credit: The Character of Credit Scoring. Representations, 126(1), 31–57.
O’Neil, C. (2017). Weapons of Math Destruction: How big data increases inequality and threatens democracy. Penguin Books.
Robinson, C. J. (2000). Black Marxism: The Making of the Black Radical Tradition. University of North Carolina Press.
Wang, J. (2018). Carceral Capitalism (Issue Vol. 21). Semiotext[e].


Micah Musheno

Whitney Museum of American Art, United States of America;nPratt Institute, School of Information, United States of America

Saturday June 10, 2023 10:40am - 11:00am EDT
PS 401 (Design Center)