The purpose of XKDD, eXaplaining Knowledge Discovery in Data Mining, is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining and machine learning. XKDD is an event organized into two moments: a tutorial to introduce audience to the topic, and a workshop to discuss recent advances in the research field. The tutorial will provide a broad overview of the state of the art on the major applications for explainable and transparent approaches and their relationship with fairness and privacy. Moreover, it will present Python/R libraries that practically shows how explainability and fairness tasks can be addressed. The workshop will seek top-quality submissions addressing uncovered important issues related to ethical, fair, explainable and transparent data mining and machine learning. Papers should present research results in any of the topics of interest for the workshop as well as application experiences, tools and promising preliminary ideas. XKDD asks for contributions from researchers, academia and industries, working on topics addressing these challenges primarily from a technical point of view, but also from a legal, ethical or sociological perspective.