  Despite significant progress, evaluation of explainable artificial
intelligence remains elusive and challenging. In this paper we propose a
fine-grained validation framework that is not overly reliant on any one facet
of these sociotechnical systems, and that recognises their inherent modular
structure: technical building blocks, user-facing explanatory artefacts and
social communication protocols. While we concur that user studies are
invaluable in assessing the quality and effectiveness of explanation
presentation and delivery strategies from the explainees' perspective in a
particular deployment context, the underlying explanation generation mechanisms
require a separate, predominantly algorithmic validation strategy that accounts
for the technical and human-centred desiderata of their (numerical) outputs.
Such a comprehensive sociotechnical utility-based evaluation framework could
allow to systematically reason about the properties and downstream influence of
different building blocks from which explainable artificial intelligence
systems are composed -- accounting for a diverse range of their engineering and
social aspects -- in view of the anticipated use case.
