Classification and regression under fairness constraints

Artificial intelligence (AI) is increasingly shaping the decisions that affect our lives—from hiring and education to healthcare and access to social services. While AI promises efficiency and objectivity, it also carries the risk of perpetuating and even amplifying societal biases embedded in the data used to train these systems.Algorithmic fairness aims to design and analyze algorithms capable of providing predictions that are both reliable and equitable.

 

 

In this talk, I will introduce one of the main approaches to achieving this goal: statistical fairness. After outlining the basic principles of this approach, I will focus specifically on a fairness criterion known as "demographic parity," which seeks to ensure that the distribution of predictions is identical across different populations. I will then discuss recent results related to regression and classification problems under this fairness constraint, exploring scenarios where differentiated treatment of populations is either permitted or prohibited.