Michal Moshkovitz -Building the Foundations of Explainable and Interpretable Machine Learning
Machine learning (ML) is integrated into our society, it is present in the judicial, health, transportation, and financial systems. As the integration increases, the necessity of ML transparency increases. The fields of explainable and interpretable ML attempt to add transparency to ML: either by adding explanations to a given black-box ML model or by building a model which is interpretable and self-explanatory.