Conference Agenda

The use of Artificial Intelligence and Machine Learning algorithms to aid Decision Making is very widespread. State-of-the-art algorithms such as Random Forest, XGBoost and Deep Learning are built in the pursuit of high accuracy. However, it is not easy to explain how these powerful algorithms arrive at their predictions. There are well documented examples in which this lack of algorithmic transparency has had a negative impact on citizens’ lives. The opaqueness may hide unfair outcomes for risk groups, such as the lack of equal opportunities in access to social services, lending decisions or parole applications. Therefore, there is an urgent need to strike a balance between three goals, namely, accuracy, explainability and fairness. In this presentation, and with an Operations Research perspective, we will navigate through some novel Machine Learning models that embed explainability and fairness in their training.