  For a long time, machine learning (ML) has been seen as the abstract problem
of learning relationships from data independent of the surrounding settings.
This has recently been challenged, and methods have been proposed to include
external constraints in the machine learning models. These methods usually come
from application-specific fields, such as de-biasing algorithms in the field of
fairness in ML or physical constraints in the fields of physics and
engineering. In this paper, we present and discuss a conceptual high-level
model that unifies these approaches in a common language. We hope that this
will enable and foster exchange between the different fields and their
different methods for including external constraints into ML models, and thus
leaving purely data-centric approaches.
