Machine Learning (ML) algorithms are known to suffer from various issues when it comes to their trustworthiness. This can hinder their deployment in sensitive application domains in practice. But how much of this problem is due to limitations in available data and/or limitations in compute (or memory)? In this workshop, we will look at this question from both a theoretical perspective, to understand where fundamental limitations exist, and from an applied point of view, to investigate which issues we can mitigate by scaling up our datasets and computer architectures.