Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective

In the last decade it became increasingly apparent the inability of technical metrics to well characterize the behavior of intelligent systems. In fact, they are nowadays requested to meet also ethical requirements such as explainability, fairness, robustness, and privacy increasing our trust in their use in the wild. The final goal is to be able to develop a new generation of more responsible and trustworthy machine learning. In this paper, we focus our attention on randomized machine learning algorithms and models questioning, from a theoretical perspective, if it is possible to simultaneously optimize multiple metrics that are in tension between each other towards randomized machine learning algorithms that we can trust. For this purpose we will leverage the most recent advances coming from the statistical learning theory: distribution stability and differential privacy.