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- Fecha de creación 14/12/2024
- Última actualización 03/01/2025
Author: Jose Luis Bustelo Gracia
Prof. Master in Financial Planning (UEMC)
This article examines algorithmic bias in artificial intelligence (AI) and its implications for global security and criminal policy. It analyses how inherent biases in data and in the design of AI models can perpetuate systemic inequalities, disproportionately affecting vulnerable groups. Through a comprehensive literature review and critical analysis of specific cases, the main sources of these biases and their ethical and social consequences are identified. Furthermore, strategies to mitigate these biases are proposed, such as improving data diversity, implementing algorithmic audits, and developing international regulatory frameworks that promote ethical and responsible use of AI. Finally, the importance of international cooperation to address these challenges and ensure that AI is used as a tool to promote justice and equity is underlined.
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