EMERGING THEORIST SPOTLIGHT: DAVON NORRIS, PH.D

I am an economic sociologist who tries to understand how our tools for determining what is valuable, worthwhile, or good are implicated in patterns of inequality with an acute concern for racial inequality. While I had a brief detour using the NBA as a case, most of my work tackles this broad theoretical question by focusing on the scores and ratings that animate credit and financial markets. Here, my published work highlights the political biases that permeate the construction of city credit ratings (Norris 2022) as well as the ways that even without those biases city credit ratings embed racism and perpetuate racial inequality between cities through the particular inputs used to create those ratings (Norris 2021).

​This latter work on racism in ratings highlighted how common approaches to identifying racial inequality in social science research fail to grasp how racism is institutionalized in an era of ratings and scores. 

Davon Norris is an LSA Collegiate Fellow and incoming Assistant Professor Organizational Studies (beginning 2024) at the University of Michigan.

Typically, analyses rely heavily on the existence of statistically significant race variable in regression to provide evidence of pernicious racial inequality, but these largely do not exist in ratings and scores because those characteristics are not used as overt inputs. This led me to questions I am grappling with in my current project that center race and consumer credit scores. ​

In consumer credit, there has been a growing consensus that using more data in credit scores is a way to promote racial inclusion despite claims that algorithms are fundamentally racist. I analyze congressional hearings, regulatory documents, and antidiscrimination court cases from 1968-2019 to assess how a range of actors cognized inequality and came to understand what constituted pernicious racial inequality in credit scores. This analysis highlights how foundational legislation in credit markets instituted a narrow logic of discrimination that became increasingly narrow throughout the late 20th century. Legal doctrine and regulatory enforcement evolved to identify racial inequality primarily through a reliance on statistical evidence and significant race variables in regression displacing attention to the ways racism manifested through non-race variables. Because significant race variables did not exist in analyses of credit scores, legal institutions and politicians grew increasingly unable to critique credit scores as racially unequal despite a pervasiveness of racial inequality. I argue this epistemological inability for institutions to cognize inequality cleared the way for growing comfort for expanding the data used in scores under a banner of racial inclusion by narrowly bounding what counts as racial exclusion. Ultimately, this project suggests that contemporary problems of algorithms and racial inequality may be less about whether algorithms like credit scores perpetuate racism and more about how we come to know if they do or not. 
 
Davon Norris is an LSA Collegiate Fellow and incoming Assistant Professor Organizational Studies (beginning 2024) at the University of Michigan. His work engages questions related to economic sociology, valuation, race, and inequality. His research has been published in outlets such as Social Forces, Socio-Economic Review, Social Problems, and Sociological Forum, and has received awards from the Future of Privacy Forum and American Sociological Association. Davon is a 3-time Buckeye earning his B.S. in Accounting (2014) and his M.A. (2018) and Ph.D. (2022) in Sociology from THE Ohio State University.
 
References
Norris, Davon. 2021. “Embedding Racism: City Credit Ratings and the Institutionalization of Race in Markets.” Social Problems (Forthcoming). doi: https://doi.org/10.1093/socpro/spab066.
Norris, Davon. 2022. “The Illusion of Transparency: The Political Double Standard in City Credit Ratings.” Socio-Economic Review (Forthcoming). doi: https://doi.org/10.1093/ser/mwac016.