In this paper, we propose standard statistical tools as a solution to commonly highlighted problems in the explainability literature. Indeed, leveraging statistical estimators allows for a proper definition of explanations, enabling theoretical guarantees and the formulation of evaluation metrics to quantitatively assess the quality of explanations. This approach circumvents, among other things, the subjective human assessment currently prevalent in the literature. Moreover, we argue that uncertainty quantification is essential for providing robust and trustworthy explanations, and it can be achieved in this framework through classical statistical procedures such as the bootstrap. However, it is crucial to note that while Statistics offers valuable contributions, it is not a panacea for resolving all the challenges. Future research avenues could focus on open problems, such as defining a purpose for the explanations or establishing a statistical framework for counterfactual or adversarial scenarios.

翻译：在本文中，我们提出将标准统计工具作为可解释性文献中常见问题的解决方案。事实上，借助统计估计量能够对解释进行恰当定义，从而提供理论保障并构建评估指标以定量衡量解释质量。该方法可避免当前文献中普遍存在的主观人为评估等问题。此外，我们认为不确定性量化对于提供稳健可信的解释至关重要，而通过自举法等经典统计程序可在此框架内实现该目标。然而需特别指出，尽管统计学做出了宝贵贡献，但它并非解决所有挑战的万能良方。未来研究方向可聚焦于开放性问题，例如为解释定义明确目标，或为反事实及对抗场景建立统计框架。