mlsanityData to 7 Oct 2026

Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test

arXiv 2401.06465 ↗First posted 12 Jan 2024cs.AI cs.LG stat.MEcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 38 of its 52 references that are in the map. First posted on arXiv 12 Jan 2024.

Builds on

38 of 52 references.

References · 38

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26 more
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Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 6.

9 papers.

  1. 2024CoSy: Evaluating Textual Explanations of Neuronscited by 17
  2. 2024Benchmarking XAI Explanations with Human-Aligned Evaluationscited by 8
  3. 2024Provably Better Explanations with Optimized Aggregation of Feature Attributionscited by 6
  4. 2025Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliancecited by 6
  5. 2025On the Effectiveness of Methods and Metrics for Explainable AI in Remote Sensing Image Scene Classificationcited by 3
  6. 2025Explainable embeddings with Distance Explainercited by 1
  7. 2025Back to the Baseline: Examining Baseline Effects on Explainability Metricscited by 1
  8. 2025Unifying Perplexing Behaviors in Modified BP Attributions through Alignment Perspectivecited by 0
  9. 2025Value bounds and Convergence Analysis for Averages of LRP attributionscited by 0