The Model Parameter Randomisation Test (MPRT) is highly recognised in the
eXplainable Artificial Intelligence (XAI) community due to its fundamental
evaluative criterion: explanations should be sensitive to the parameters of the
model they seek to explain. However, recent studies have raised several
methodological concerns for the empirical interpretation of MPRT. In response,
we propose two modifications to the original test: Smooth MPRT and Efficient
MPRT. The former reduces the impact of noise on evaluation outcomes via
sampling, while the latter avoids the need for biased similarity measurements
by re-interpreting the test through the increase in explanation complexity
after full model randomisation. Our experiments show that these modifications
enhance the metric reliability, facilitating a more trustworthy deployment of
explanation methods.

我们提出了两种对原始测试的修改：平滑 MPRT 和高效 MPRT。前者通过采样减少噪声对评估结果的影响，后者通过完全模型随机化后解释复杂性的增加来避免对相似性测量的有偏倚需求。我们的实验证明这些修改提高了度量信度，更可靠地部署解释方法。

审视显著图的常规检查

A Fresh Look at Sanity Checks for Saliency Maps

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.

通过引入 Smooth MPRT 和 Efficient MPRT 两种改进方法，本文提出了对 Model Parameter Randomisation Test (MPRT) 进行适应的新方法，以改善评估结果的可靠性，从而实现对 eXplainable Artificial Intelligence（XAI）方法的更可信赖的应用。