There is general consensus that it is important for artificial intelligence
(AI) and machine learning systems to be explainable and/or interpretable.
However, there is no general consensus over what is meant by 'explainable' and
'interpretable'. In this paper, we argue that this lack of consensus is due to
there being several distinct stakeholder communities. We note that, while the
concerns of the individual communities are broadly compatible, they are not
identical, which gives rise to different intents and requirements for
explainability/interpretability. We use the software engineering distinction
between validation and verification, and the epistemological distinctions
between knowns/unknowns, to tease apart the concerns of the stakeholder
communities and highlight the areas where their foci overlap or diverge. It is
not the purpose of the authors of this paper to 'take sides' - we count
ourselves as members, to varying degrees, of multiple communities - but rather
to help disambiguate what stakeholders mean when they ask 'Why?' of an AI.

本文旨在阐明 AI 与机器学习系统的解释和可解释性的概念及其区别，并通过验证 / 确认的角度和已知 / 未知的分工，探讨相关利益相关方的关切和需求。