Inteligencia artificial y equidad de género

Abstract

The advancement of artificial intelligence (AI) has transformed multiple fields, including decision-making in key sectors such as employment, justice, and education. However, its development and application have revealed gender biases that perpetuate structural inequalities. This article examines how AI algorithms can reproduce historical discrimination due to biases in training data and the lack of diversity in their design. Through a normative and jurisprudential approach, it analyzes regulatory frameworks aimed at ensuring more equitable and transparent AI. Additionally, it proposes strategies to mitigate biases, such as implementing algorithmic audits and promoting diversity in data engineering. The research concludes that it is imperative to adopt intersectional approaches and specific regulations to ensure that AI contributes to gender equity rather than reinforcing existing gaps.