Ethics and bias mitigation in AI systems: Technical and regulatory trends
Ética y mitigación de sesgos en sistemas de IA: Tendencias técnicas y regulatorias
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Abstract
This article critically analyzes technical and regulatory strategies to mitigate biases in artificial intelligence (AI) systems, an urgent challenge given the social impact of these technologies. Through a documentary review of articles in Scopus (2018–2022) in Spanish and English, four thematic axes were identified: bias detection techniques, mitigation methods (data rebalancing, adversarial debiasing), international regulatory frameworks (EU, U.S., OECD), and challenges in real-world implementation (trade-offs between equity and performance). The results reveal that, despite advances in fairness-aware algorithms, gaps persist between theory and practice, especially in industrial contexts. It is concluded that ethics in AI requires multidisciplinary approaches that integrate technical solutions with adaptable governance, community participation, and continuous audits
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