  Explainability is becoming an important requirement for organizations that
make use of automated decision-making due to regulatory initiatives and a shift
in public awareness. Various and significantly different algorithmic methods to
provide this explainability have been introduced in the field, but the existing
literature in the machine learning community has paid little attention to the
stakeholder whose needs are rather studied in the human-computer interface
community. Therefore, organizations that want or need to provide this
explainability are confronted with the selection of an appropriate method for
their use case. In this paper, we argue there is a need for a methodology to
bridge the gap between stakeholder needs and explanation methods. We present
our ongoing work on creating this methodology to help data scientists in the
process of providing explainability to stakeholders. In particular, our
contributions include documents used to characterize XAI methods and user
requirements (shown in Appendix), which our methodology builds upon.
