  Explanation in machine learning and related fields such as artificial
intelligence aims at making machine learning models and their decisions
understandable to humans. Existing work suggests that personalizing
explanations might help to improve understandability. In this work, we derive a
conceptualization of personalized explanation by defining and structuring the
problem based on prior work on machine learning explanation, personalization
(in machine learning) and concepts and techniques from other domains such as
privacy and knowledge elicitation. We perform a categorization of explainee
data used in the process of personalization as well as describing means to
collect this data. We also identify three key explanation properties that are
amendable to personalization: complexity, decision information and
presentation. We also enhance existing work on explanation by introducing
additional desiderata and measures to quantify the quality of personalized
explanations.
