The Model Parameter Randomisation Test (MPRT) is widely acknowledged in the eXplainable Artificial Intelligence (XAI) community for its well-motivated evaluative principle: that the explanation function should be sensitive to changes in the parameters of the model function. However, recent works have identified several methodological caveats for the empirical interpretation of MPRT. To address these caveats, we introduce two adaptations to the original MPRT -- Smooth MPRT and Efficient MPRT, where the former minimises the impact that noise has on the evaluation results through sampling and the latter circumvents the need for biased similarity measurements by re-interpreting the test through the explanation's rise in complexity, after full parameter randomisation. Our experimental results demonstrate that these proposed variants lead to improved metric reliability, thus enabling a more trustworthy application of XAI methods.

翻译：模型参数随机化测试（MPRT）因其评估原则的充分动机而被可解释人工智能（XAI）领域广泛认可：即解释函数应能敏感地响应模型函数参数的变化。然而，近期研究指出了MPRT经验解释中存在的若干方法论缺陷。为解决这些问题，我们引入了对原始MPRT的两种改进——平滑MPRT与高效MPRT，前者通过采样最小化噪声对评估结果的影响，后者则通过完全参数随机化后依据解释复杂度的提升重新诠释测试，从而规避了有偏相似性度量的需求。实验结果表明，所提出的变体方案提升了评估指标的可靠性，进而增强了XAI方法的可信应用能力。