Seminario 20/25: Dolores Romero Morales (Copenhagen Business School, Denmark): Explaining Machine Learning Outcomes by Means of Mathematical Optimization
There is a growing literature on enhancing the interpretability of Machine Learning methods involved in Data Driven Decision Making. Interpretability is desirable for non-experts; it is required by regulators for models aiding, for instance, credit scoring; and since 2018 the European Union has extended this requirement by imposing the so-calledright-to-explanation in algorithmic decision making. Mathematical…
