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.

本研究提出使用标准统计工具来解决说明性文献中普遍存在的问题，通过利用统计估计器来定义解释，从而实现理论保证和评估指标的制定，以定量评估解释的质量。此方法避免了目前文献中普遍存在的主观人为评估。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，并且可以通过诸如自助法等经典统计方法在这个框架中实现。然而，需要注意的是，虽然统计学提供了宝贵的贡献，但它并不能解决所有问题。未来的研究方向可以聚焦于一些开放性问题，如为解释定义目的或建立适用于对策或对抗场景的统计框架。