Algorithmic Opacity, Proxy Discrimination and Financial Citizenship in AI-Driven Credit Markets

Abstract

Abstract: This article investigates the intersection of artificial intelligence, consumer credit, and financial governance, arguing that algorithmic opacity in AI-driven credit scoring does not eliminate discrimination but transforms it into an epistemic and governance crisis. Drawing on doctrinal legal analysis, comparative regulatory examination of Brazil, the European Union, and the United States, and an analytical reconstruction of the Apple Card case, the study demonstrates that historical biases are statistically reconstructed through proxy variables while the inherent opacity of machine learning models conceals these processes under a veneer of data-driven objectivity. The result is a structural deficit in evidence, contestability, and supervisory capacity that undermines the core pillars of financial citizenship: protection, participation, and trust. The original contribution lies in reconceptualizing algorithmic credit discrimination as a problem produced by opacity rather than solely by intentional disparate treatment, revealing that the absence of evidence of bias cannot be equated with evidence of fairness when the system under scrutiny is designed in ways that resist independent verification.