Banca de DEFESA: Cristiane Ferreira Kovalski de Moura

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : Cristiane Ferreira Kovalski de Moura
DATE: 04/09/2025
TIME: 14:00
LOCAL: https://meet.google.com/ort-amcn-iie
TITLE:

ALGORITHMIC RACISM IN CREDIT FOR BLACK BUSINESSES IN BRAZIL: A CONTEMPORARY ANALYSIS.

KEY WORDS:

Algorithmic racismo, Black-owned businesses, Credit assessment ou Credit evaluation, Access to credit.

PAGES: 183
BIG AREA: Ciências Sociais Aplicadas
AREA: Direito
SUMMARY:

The expansion of algorithmic decision-making across various social sectors has intensified debates about its ethical and social impacts. In the financial field, particularly in risk assessment and credit granting, these computational systems operate based on historical data and statistical patterns that often reproduce pre-existing racial discrimination. This has led to a deepening of socioeconomic inequalities, especially concerning access to credit for Black entrepreneurs.National and international research indicates that algorithmic racism is a contemporary form of structural racism, operating in an invisible and automated manner. Unlike traditional forms of discrimination, algorithmic racism manifests when supposedly neutral systems are trained on biased data, often stemming from historically exclusionary social structures. As a result, Black individuals—and particularly Black entrepreneurs—are evaluated more stringently, receive worse financing conditions, or do not have their applications reviewed at all, even when presenting technical requirements similar to those of other racial groups.The marginalization of these entrepreneurs occurs through various factors: the scarcity of positive data on their economic activities; the informality that distances them from formal monitoring systems; and the very credit scoring systems that inadequately account for the financial realities of Black populations. Even when formalized as individual microentrepreneurs (MEIs) or microenterprises, many Black business owners struggle to prove their creditworthiness and payment history in a banking system that prioritizes standardized profiles with formal employment and assets as collateral.Case studies by Safiya Umoja Noble, Virginia Eubanks, Ruha Benjamin, and Tarcízio Silva show that algorithms applied to credit analysis replicate a logic of exclusion that, although automated, is not neutral. On the contrary, it is influenced by historical criteria of racial, economic, and territorial discrimination. This is evident in the analysis of risk assessment systems that penalize peripheral addresses, lower educational levels, absence of formal employment, and other variables often associated with the Black population. In light of this scenario, research on algorithmic racism in credit highlights an urgent need for regulation and transparency in the models used by financial institutions. The lack of oversight and clear criteria on how algorithms are developed, validated, and audited prevents Black entrepreneurs from legally contesting discriminatory decisions. Furthermore, there is a significant gap in racial diversity within the teams responsible for building these technologies. In summary, research on algorithmic racism in credit access for Black entrepreneurs exposes a silent injustice that can only be addressed through greater transparency, appropriate regulation, inclusion of diversity in technological processes, and public policies for redress. Overcoming these barriers is crucial to fostering a fairer and more inclusive economy in Brazil and worldwide.

COMMITTEE MEMBERS:
Presidente - 1647964 - VALCIR GASSEN
Interna - 1150035 - FERNANDA DE CARVALHO LAGE
Interna - 4878654 - LIVIA GIMENES DIAS DA FONSECA
Externo à Instituição - MARCOS VINÍCIUS LUSTOSA QUEIROZ - IDP
Externo à Instituição - Rafael de Deus Garcia - IDP