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Gender and algorithmic bias in artificial intelligence
Overview
Discussions regarding artificial intelligence have increasingly focused on the presence of systemic bias within large language models and algorithms. Researchers at the University of A Coruña noted that AI models are not free of bias because they are trained on internet content containing existing inequalities and hate speech, which leads to errors that “systematically affect specific demographic groups or contexts.”
This issue extends to practical applications in recruitment, advertising, and performance measurement. Critics argue that these systems often inherit historical class and gender disparities, treating past social imbalances as objective standards. For example, recruitment algorithms may favor profiles mirroring historically male-dominated leadership, while targeted advertising can reinforce stereotypes by directing specific types of ads toward different genders.
A June 2026 study by UN Women examined 133 artificial intelligence systems, finding that 44% exhibited gender bias and 26% demonstrated a combination of both gender and racial bias.
Entities
Timeline
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[TECHNOLOGY] 2 sourcesAlgorithmic bias reproduces gender discrimination in digital era
A study by UN Women reveals that 44% of analyzed AI systems exhibit gender bias, as algorithms in recruitment and advertising often reproduce historical social and economic inequalities.
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[TECHNOLOGY] 2 sourcesResearchers debate gender bias and language in AI
Researchers in A Coruña discussed how gender bias and human decision-making influence artificial intelligence, noting that models often replicate inequalities found in their training data.
Sources
annahjaddimocrati.org · forceofnature.com · laopinioncoruna.es · lasemainedansleboulonnais.fr