# Metamorphic Testing with the Rashomon Set: Explanation Faithfulness in Machine Learning

arXiv: https://arxiv.org/abs/2606.06056 · HTML: https://mlsanity.com/paper/2606.06056/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [Evaluating the visualization of what a Deep Neural Network has learned](https://mlsanity.com/paper/1509.06321/) (2015)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Predictive Multiplicity in Classification](https://mlsanity.com/paper/1909.06677/) (2019)
- [On the Existence of Simpler Machine Learning Models](https://mlsanity.com/paper/1908.01755/) (2019)
- [Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing](https://mlsanity.com/paper/1808.05353/) (2018)
- [An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning](https://mlsanity.com/paper/2306.15786/) (2023)
- [Adaptive Metamorphic Testing with Contextual Bandits](https://mlsanity.com/paper/1910.00262/) (2019)
- [Using Metamorphic Relations to Verify and Enhance Artcode Classification](https://mlsanity.com/paper/2108.02694/) (2021)
- [Metamorphic Testing of a Deep Learning based Forecaster](https://mlsanity.com/paper/1907.06632/) (2019)
- [Evaluating Human Trajectory Prediction with Metamorphic Testing](https://mlsanity.com/paper/2407.18756/) (2024)
- [Metamorphic Testing for Pose Estimation Systems](https://mlsanity.com/paper/2502.09460/) (2025)
- [Rashomon in the Streets: Explanation Ambiguity in Scene Understanding](https://mlsanity.com/paper/2509.03169/) (2025)
- [Metamorphic Testing of Multimodal Human Trajectory Prediction](https://mlsanity.com/paper/2509.01294/) (2025)

## Abstract (arXiv)

Multiple machine learning models can achieve near-equivalent predictive performance on the same task, yet provide divergent feature-based explanations. This is called the Rashomon effect of (explainable) machine learning, and it raises the question of which explanations, if any, are trustworthy. We propose a framework based on metamorphic testing that assesses explanation faithfulness without requiring ground-truth labels by exploring attributed feature importance from post-hoc explanation methods. Five metamorphic relations formalize expected consistency properties between model behavior and feature attributions. We apply this general framework to two tabular regression datasets and two post-hoc explainers (SHAP and LIME) to demonstrate the approach. The framework offers a practical, model-agnostic tool for selecting accurate models with reliable and trustworthy explanations.