This paper argues that interpretability research in Artificial Intelligence is fundamentally ill-posed as existing definitions of interpretability are not *actionable*: they fail to provide formal principles from which concrete modelling and inferential rules can be derived. We posit that for a definition of interpretability to be actionable, it must be given in terms of *symmetries*. We hypothesise that four symmetries suffice to (i) motivate core interpretability properties, (ii) characterize the class of interpretable models, and (iii) derive a unified formulation of interpretable inference (e.g., alignment, interventions, and counterfactuals) as a form of Bayesian inversion.

翻译：本文认为，人工智能领域的可解释性研究在根本上是不适定的，因为现有的可解释性定义不具备*可操作性*：它们未能提供可推导出具体建模与推理规则的形式化原则。我们主张，要使可解释性的定义具备可操作性，就必须基于*对称性*来构建。我们假设，四种对称性足以（i）激发核心的可解释性特性，（ii）刻画可解释模型的类别，以及（iii）推导出将可解释性推理（例如，对齐、干预和反事实）作为一种贝叶斯逆问题形式的统一表述。