  Automated machine learning (AutoML) systems aim at finding the best machine
learning (ML) pipeline that automatically matches the task and data at hand. We
investigate the robustness of machine learning pipelines generated with three
AutoML systems, TPOT, H2O, and AutoKeras. In particular, we study the influence
of dirty data on accuracy, and consider how using dirty training data may help
create more robust solutions. Furthermore, we also analyze how the structure of
the generated pipelines differs in different cases.
