AdONE Seminar: Prof. Dolores Romero Morales (Copenhagen Business School)

There is a pressing need to make Machine Learning tools more transparent. Despite excellent accuracy, state-of-the-art Machine Learning models effectively work as black boxes, which hinders model validation and may hide unfair outcomes for risk groups. Transparency is of particular importance for high-stakes decisions, is required by regulators for models aiding, for instance, credit scoring, and since 2018 the EU has extended this requirement by imposing the so-called right-to-explanation in algorithmic decision-making. From the Mathematical Optimization perspective, this means that we need to strike a balance between two objectives, namely accuracy and transparency. In this presentation, we will navigate through some novel techniques embedded in the construction of Machine Learning models to enhance their transparency. This includes the ability to provide global, local and counterfactual explanations, as well as model cost-sensitivity and fairness requirements.