January 30, 2025 at 11 AM EST

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.

Meet the Experts

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.

Academic License
Gurobi provides free, full-featured licenses for coursework, teaching, and research at degree-granting academic institutions. Academics can receive guidance and support through our Community Forum.

Search

Gurobi Optimization