  AutoML systems are currently rising in popularity, as they can build powerful
models without human oversight. They often combine techniques from many
different sub-fields of machine learning in order to find a model or set of
models that optimize a user-supplied criterion, such as predictive performance.
The ultimate goal of such systems is to reduce the amount of time spent on
menial tasks, or tasks that can be solved better by algorithms while leaving
decisions that require human intelligence to the end-user. In recent years, the
importance of other criteria, such as fairness and interpretability, and many
others have become more and more apparent. Current AutoML frameworks either do
not allow to optimize such secondary criteria or only do so by limiting the
system's choice of models and preprocessing steps. We propose to optimize
additional criteria defined by the user directly to guide the search towards an
optimal machine learning pipeline. In order to demonstrate the need and
usefulness of our approach, we provide a simple multi-criteria AutoML system
and showcase an exemplary application.
