  Automated decision systems are increasingly used for consequential decision
making -- for a variety of reasons. These systems often rely on sophisticated
yet opaque models, which do not (or hardly) allow for understanding how or why
a given decision was arrived at. This is not only problematic from a legal
perspective, but non-transparent systems are also prone to yield undesirable
(e.g., unfair) outcomes because their sanity is difficult to assess and
calibrate in the first place. In this work, we conduct a study to evaluate
different attempts of explaining such systems with respect to their effect on
people's perceptions of fairness and trustworthiness towards the underlying
mechanisms. A pilot study revealed surprising qualitative insights as well as
preliminary significant effects, which will have to be verified, extended and
thoroughly discussed in the larger main study.
