  Machine Learning algorithms are technological key enablers for artificial
intelligence (AI). Due to the inherent complexity, these learning algorithms
represent black boxes and are difficult to comprehend, therefore influencing
compliance behavior. Hence, compliance with the recommendations of such
artifacts, which can impact employees' task performance significantly, is still
subject to research - and personalization of AI explanations seems to be a
promising concept in this regard. In our work, we hypothesize that, based on
varying backgrounds like training, domain knowledge and demographic
characteristics, individuals have different understandings and hence mental
models about the learning algorithm. Personalization of AI explanations,
related to the individuals' mental models, may thus be an instrument to affect
compliance and therefore employee task performance. Our preliminary results
already indicate the importance of personalized explanations in industry
settings and emphasize the importance of this research endeavor.
