Ante-hoc interpretability has become the holy grail of explainable machine learning for high-stakes domains such as healthcare; however, this notion is elusive, lacks a widely-accepted definition and depends on the deployment context. It can refer to predictive models whose structure adheres to domain-specific constraints, or ones that are inherently transparent. The latter notion assumes observers who judge this quality, whereas the former presupposes them to have technical and domain expertise, in certain cases rendering such models unintelligible. Additionally, its distinction from the less desirable post-hoc explainability, which refers to methods that construct a separate explanatory model, is vague given that transparent predictors may still require (post-)processing to yield satisfactory explanatory insights. Ante-hoc interpretability is thus an overloaded concept that comprises a range of implicit properties, which we unpack in this paper to better understand what is needed for its safe deployment across high-stakes domains. To this end, we outline model- and explainer-specific desiderata that allow us to navigate its distinct realisations in view of the envisaged application and audience.

针对高风险领域，可解释的机器学习的先验解释性已成为其圣杯；然而，这个概念是含糊的，缺乏广泛接受的定义，并且取决于部署上下文。

高风险领域先解释可行性的(不)合理吸引力: 透明度必要但不足以实现可解释性