Explainable Machine Learning

Methods for Helping to Understand ML Models

This research line involves studying methods for helping end-users to better understand complex and frequently black-box maching learning models. Our work is currently focused on trying to explain the reasons/conditions leading to prediction errors to serve as warnings to the use of the models for critical decisions.