Do Black Fintechs Matter? The Long and Winding Road to Develop Inclusive Algorithms for Social Justice
Bibliographic record
Abstract
Racism in the financial sector is a complex global phenomenon involving intertwined social, economic, and digital systems. Credit denial rates for Black applicants suggest that financial systems have assimilated racial discrimination into their algorithms and credit scoring tools. Considering that digital tools incorporate their developers’ life experiences and perspectives, and in view of the low representation of the Black community in the digital startup universe, it is evident that even well-meaning fintechs are reproducing racial bias in their technology-intensive business models. In this paper, we investigate how three Black-owned Brazilian fintechs design and use inclusive algorithms to decrease persistent social injustice involving racial financial inclusion. We combine the concept of sociotechnical reconfiguration, taken from the South American tradition of “tecnologia social” (social technology), with the three core concepts of Nancy Fraser’s theory of social justice—representation, recognition, and redistribution—to analyze the process by which Black-owned fintechs develop their particular solutions for combating racial bias. We found that pressured by the need to be profitable, the lack of capital to develop their own credit scoring tools, and the low profile maintained by the Black community ecosystem, Black-owned fintechs focus on a sort of affirmative experimentation to gradually create original digital solutions aimed at achieving social justice in the financial system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".