  With the growing abundance of unlabeled data in real-world tasks, researchers
have to rely on the predictions given by black-boxed computational models.
However, it is an often neglected fact that these models may be scoring high on
accuracy for the wrong reasons. In this paper, we present a practical impact
analysis of enabling model transparency by various presentation forms. For this
purpose, we developed an environment that empowers non-computer scientists to
become practicing data scientists in their own research field. We demonstrate
the gradually increasing understanding of journalism historians through a
real-world use case study on automatic genre classification of newspaper
articles. This study is a first step towards trusted usage of machine learning
pipelines in a responsible way.
