Justicia en los Modelos de Inteligencia Artificial: Nueva Perspectiva Basada en el Re-diseño de Algoritmos

En los últimos años, los modelos de inteligencia artificial han demostrado un potencial increíble para transformar industrias, desde la salud hasta las finanzas. Sin embargo, también han expuesto un problema preocupante: el sesgo algorítmico.

Fairness in artificial intelligence models: how to mitigate discrimination in the presence of multiple sensitive attributes?

Let's suppose we have a machine learning model, 𝑓, that predicts the price of an insurance premium, Y, based on data that includes a sensitive attribute, such as gender. Discrimination may occur due to a statistical bias...

Trade-off between justice and adjustment: a case study of crime

The study of algorithmic justice emerged in 2011 with Cynthia Dwork [1], who based it on the principle of equal opportunity: all people, regardless of their characteristics, should be able to access the same opportunities and benefits.

Algorithmic justice and its limitations: An impossibility theorem

Algorithmic justice in learning models refers to the application of ethical and fairness principles in the development and use of machine learning algorithms.