Webinar
On a (Short) Optimization Tour Through Transparent and Fair ML with Prof. Dolores Romero Morales
In this talk, we will navigate through some of the latest advances in Mathematical Modelling and Optimization.
January 30, 2025
10:00 AM - 11:30 AM PST
Webinar
On a (Short) Optimization Tour Through Transparent and Fair ML with Prof. Dolores Romero Morales
In this talk, we will navigate through some of the latest advances in Mathematical Modelling and Optimization.
January 30, 2025
10:00 AM - 11:30 AM PST
Webinar
On a (Short) Optimization Tour Through Transparent and Fair ML with Prof. Dolores Romero Morales
In this talk, we will navigate through some of the latest advances in Mathematical Modelling and Optimization.
January 30, 2025
10:00 AM - 11:30 AM PST
Webinar Overview
There is a common consensus that state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms are powerful in terms of their accuracy, but they are also perceived as opaque not being transparent about how they arrive at their decisions. This prevents the adoption of these powerful algorithms in Data-Driven Decision-Making. Even when in place, they can have a detrimental impact on the citizen, and there are well-documented examples of discriminatory outcomes in high-stakes algorithmic decision-making. Therefore, there is an urgent need to strike a balance between three goals, namely, accuracy, explainability and fairness.
In this talk, we will navigate through some of the latest advances in Mathematical Modelling and Optimization to enhance the transparency and fairness of ML algorithms. We will first focus on the training of ML models that trade off accuracy, explainability and fairness. Then, we will focus on the task of providing explanations to an existing ML model by means of the burgeoning field of Counterfactual Analysis.
Webinar Overview
There is a common consensus that state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms are powerful in terms of their accuracy, but they are also perceived as opaque not being transparent about how they arrive at their decisions. This prevents the adoption of these powerful algorithms in Data-Driven Decision-Making. Even when in place, they can have a detrimental impact on the citizen, and there are well-documented examples of discriminatory outcomes in high-stakes algorithmic decision-making. Therefore, there is an urgent need to strike a balance between three goals, namely, accuracy, explainability and fairness.
In this talk, we will navigate through some of the latest advances in Mathematical Modelling and Optimization to enhance the transparency and fairness of ML algorithms. We will first focus on the training of ML models that trade off accuracy, explainability and fairness. Then, we will focus on the task of providing explanations to an existing ML model by means of the burgeoning field of Counterfactual Analysis.
Webinar Overview
There is a common consensus that state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms are powerful in terms of their accuracy, but they are also perceived as opaque not being transparent about how they arrive at their decisions. This prevents the adoption of these powerful algorithms in Data-Driven Decision-Making. Even when in place, they can have a detrimental impact on the citizen, and there are well-documented examples of discriminatory outcomes in high-stakes algorithmic decision-making. Therefore, there is an urgent need to strike a balance between three goals, namely, accuracy, explainability and fairness.
In this talk, we will navigate through some of the latest advances in Mathematical Modelling and Optimization to enhance the transparency and fairness of ML algorithms. We will first focus on the training of ML models that trade off accuracy, explainability and fairness. Then, we will focus on the task of providing explanations to an existing ML model by means of the burgeoning field of Counterfactual Analysis.
Speaker
Meet Your Expert Speaker
Learn from the best in the industry.
Dolores Romero Morales
Professor in Operations Research
Dolores Romero Morales is a Professor in Operations Research at Copenhagen Business School. Her areas of expertise include Data Science, Supply Chain Optimization and Revenue Management. In Data Science she investigates explainability/interpretability, fairness and visualization matters. In Supply Chain Optimization she works on environmental issues and robustness. In Revenue Management she works on large-scale network models. Her work has appeared in a variety of leading scholarly journals, including European Journal of Operational Research, Management Science, Mathematical Programming and Operations Research, and has received various distinctions. Currently, she is Editor-in-Chief to TOP, the Operations Research journal of the Spanish Society of Statistics and Operations Research, and an Associate Editor of Journal of the Operational Research Society and the INFORMS Journal on Data Science.
Dolores has received funding from the EU as well as national research councils to conduct her research. She has worked with and advised various companies on these topics, including IBM, SAS, KLM and Radisson Edwardian Hotels, as a result of which these companies managed to improve some of their practices. SAS named her an Honorary SAS Fellow and member of the SAS Academic Advisory Board.
Dolores joined Copenhagen Business School in 2014. Prior to coming to Copenhagen Business School, she was a Full Professor at University of Oxford (2003-2014) and an Assistant Professor at Maastricht University (2000-2003). She has a BSc and an MSc in Mathematics from Universidad de Sevilla and a PhD in Operations Research from Erasmus University Rotterdam.
Speaker
Meet Your Expert Speaker
Learn from the best in the industry.
Dolores Romero Morales
Professor in Operations Research
Dolores Romero Morales is a Professor in Operations Research at Copenhagen Business School. Her areas of expertise include Data Science, Supply Chain Optimization and Revenue Management. In Data Science she investigates explainability/interpretability, fairness and visualization matters. In Supply Chain Optimization she works on environmental issues and robustness. In Revenue Management she works on large-scale network models. Her work has appeared in a variety of leading scholarly journals, including European Journal of Operational Research, Management Science, Mathematical Programming and Operations Research, and has received various distinctions. Currently, she is Editor-in-Chief to TOP, the Operations Research journal of the Spanish Society of Statistics and Operations Research, and an Associate Editor of Journal of the Operational Research Society and the INFORMS Journal on Data Science.
Dolores has received funding from the EU as well as national research councils to conduct her research. She has worked with and advised various companies on these topics, including IBM, SAS, KLM and Radisson Edwardian Hotels, as a result of which these companies managed to improve some of their practices. SAS named her an Honorary SAS Fellow and member of the SAS Academic Advisory Board.
Dolores joined Copenhagen Business School in 2014. Prior to coming to Copenhagen Business School, she was a Full Professor at University of Oxford (2003-2014) and an Assistant Professor at Maastricht University (2000-2003). She has a BSc and an MSc in Mathematics from Universidad de Sevilla and a PhD in Operations Research from Erasmus University Rotterdam.
Speaker
Meet Your Expert Speaker
Learn from the best in the industry.
Professor in Operations Research
Dolores Romero Morales
Dolores Romero Morales is a Professor in Operations Research at Copenhagen Business School. Her areas of expertise include Data Science, Supply Chain Optimization and Revenue Management. In Data Science she investigates explainability/interpretability, fairness and visualization matters. In Supply Chain Optimization she works on environmental issues and robustness. In Revenue Management she works on large-scale network models. Her work has appeared in a variety of leading scholarly journals, including European Journal of Operational Research, Management Science, Mathematical Programming and Operations Research, and has received various distinctions. Currently, she is Editor-in-Chief to TOP, the Operations Research journal of the Spanish Society of Statistics and Operations Research, and an Associate Editor of Journal of the Operational Research Society and the INFORMS Journal on Data Science.
Dolores has received funding from the EU as well as national research councils to conduct her research. She has worked with and advised various companies on these topics, including IBM, SAS, KLM and Radisson Edwardian Hotels, as a result of which these companies managed to improve some of their practices. SAS named her an Honorary SAS Fellow and member of the SAS Academic Advisory Board.
Dolores joined Copenhagen Business School in 2014. Prior to coming to Copenhagen Business School, she was a Full Professor at University of Oxford (2003-2014) and an Assistant Professor at Maastricht University (2000-2003). She has a BSc and an MSc in Mathematics from Universidad de Sevilla and a PhD in Operations Research from Erasmus University Rotterdam.
