Seminar with Dolores Romero Morales: Fairness in Machine Learning: A short OR tour

Open seminar titled "Fairness in Machine Learning: A short OR tour" with Dolores Romero Morales, Department of Economics, CBS.

Despite excellent accuracy, state-of-the-art Artificial Intelligence (AI) / Machine Learning (ML) models may be unfair if not trained appropriately, and there are well-documented examples of discriminatory outcomes in high-stakes algorithmic decision-making.

In this presentation, Dolores Morales will navigate through some novel techniques that embed fairness in the construction of ML models. She will discuss a number of Multi-Objective Optimization formulations that strike a balance between accuracy and different measures of fairness.  

To read Professor Morales newest research on the topic, see "On Enhancing the Explainability and Fairness of Tree Ensembles", published in January 2025 in European Journal of Operational Research.

About the speaker

Dolores Romero Morales is a Professor of Operations Research at Copenhagen Business School, specializing in Data Science, Supply Chain Optimization, and Revenue Management. Her research focuses on topics like explainable AI, fairness, environmental sustainability, and large-scale network models. She has published in top journals like Management Science and Operations Research and serves as Editor-in-Chief of TOP, the journal of the Spanish Society of Statistics and Operations Research.

Dolores has collaborated with companies like IBM, SAS, and KLM to enhance their practices, earning distinctions such as Honorary SAS Fellow. She currently leads the EU-funded NeEDS project, driving advancements in data-driven decision-making. Before joining CBS in 2014, she was a professor at the University of Oxford and holds a PhD in Operations Research from Erasmus University Rotterdam.

How to participate?

No registration needed, simply show up.

The seminar is open to all.