Intersectionality and algorithmic discrimination
Year: 2026
Type: article
Abstract: This article analyzes how identity-based discrimination and social exclusion processes are reconfigured by algorithmic social differentiation, proposing a framework that links social systems theory with intersectionality as a critical and normative basis for legal responses. The increasing use of algorithms reproduces and amplifies historical inequalities, while the unidimensional categories of anti-discrimination law prove insufficient to capture the complexity of digital exclusion across multiple, interconnected social markers. A qualitative approach involving theoretical-conceptual reconstruction was employed, integrating social systems theory with the analytical perspective of intersectionality. The study demonstrates that algorithmic differentiation recombines classic forms of social differentiation through psychographic profiling, ranking, and the predictive control of communication flows. Algorithmic discrimination is systemic and polycontextual in nature, functioning as an automated reproduction of historical asymmetries rather than a mere technical glitch. The article concludes that addressing this exclusion requires moving beyond simplified views of equality and calls for multidimensional mechanisms—such as explainable transparency, independent auditability, meaningful human review, enhanced evidentiary standards, and democratic participation in technological governance.
Author: Mateus de Oliveira Fornasier
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7225652507
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