  National and international guidelines for trustworthy artificial intelligence
(AI) consider explainability to be a central facet of trustworthy systems. This
paper outlines a multi-disciplinary rationale for explainability auditing.
Specifically, we propose that explainability auditing can ensure the quality of
explainability of systems in applied contexts and can be the basis for
certification as a means to communicate whether systems meet certain
explainability standards and requirements. Moreover, we emphasize that
explainability auditing needs to take a multi-disciplinary perspective, and we
provide an overview of four perspectives (technical, psychological, ethical,
legal) and their respective benefits with respect to explainability auditing.
