  Understanding when and why to apply any given eXplainable Artificial
Intelligence (XAI) technique is not a straightforward task. There is no single
approach that is best suited for a given context. This paper aims to address
the challenge of selecting the most appropriate explainer given the context in
which an explanation is required. For AI explainability to be effective,
explanations and how they are presented needs to be oriented towards the
stakeholder receiving the explanation. If -- in general -- no single
explanation technique surpasses the rest, then reasoning over the available
methods is required in order to select one that is context-appropriate. Due to
the transparency they afford, we propose employing argumentation techniques to
reach an agreement over the most suitable explainers from a given set of
possible explainers.
  In this paper, we propose a modular reasoning system consisting of a given
mental model of the relevant stakeholder, a reasoner component that solves the
argumentation problem generated by a multi-explainer component, and an AI model
that is to be explained suitably to the stakeholder of interest. By formalising
supporting premises -- and inferences -- we can map stakeholder characteristics
to those of explanation techniques. This allows us to reason over the
techniques and prioritise the best one for the given context, while also
offering transparency into the selection decision.
