Justice in Artificial Intelligence Models: A New Perspective Based on Algorithm Redesign

In recent years, artificial intelligence models have demonstrated incredible potential to transform industries, from healthcare to finance. However, they have also exposed a troubling issue: algorithmic bias.

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.