# Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML

arXiv: https://arxiv.org/abs/2507.14744 · HTML: https://mlsanity.com/paper/2507.14744/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 19 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01602/) (2022)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [On the Existence of Simpler Machine Learning Models](https://mlsanity.com/paper/1908.01755/) (2019)
- [Predictive Multiplicity in Probabilistic Classification](https://mlsanity.com/paper/2206.01131/) (2022)
- [A Path to Simpler Models Starts With Noise](https://mlsanity.com/paper/2310.19726/) (2023)
- [Learning Curves for Decision Making in Supervised Machine Learning: A Survey](https://mlsanity.com/paper/2201.12150/) (2022)
- [An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning](https://mlsanity.com/paper/2306.15786/) (2023)
- [Position: Explain to Question not to Justify](https://mlsanity.com/paper/2402.13914/) (2024)
- [Predictive Churn with the Set of Good Models](https://mlsanity.com/paper/2402.07745/) (2024)
- [An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification](https://mlsanity.com/paper/2405.01557/) (2024)
- [Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective](https://mlsanity.com/paper/2412.09712/) (2024)
- [Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective](https://mlsanity.com/paper/2303.13299/) (2023)
- [The Disagreement Problem in Faithfulness Metrics](https://mlsanity.com/paper/2311.07763/) (2023)
- [Dissenting Explanations: Leveraging Disagreement to Reduce Model Overreliance](https://mlsanity.com/paper/2307.07636/) (2023)
- [When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances](https://mlsanity.com/paper/2406.14349/) (2024)
- [The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity](https://mlsanity.com/paper/2407.13070/) (2024)
- [Human-Centric eXplainable AI in Education](https://mlsanity.com/paper/2410.19822/) (2024)
- [Finding Patterns in Ambiguity: Interpretable Stress Testing in the Decision~Boundary](https://mlsanity.com/paper/2408.06302/) (2024)
- [When Explanations Compete: Policy-Aware Selection Under Uncertainty](https://mlsanity.com/paper/2410.05479/) (2024)

## Built on by (most cited first, 6 of 6)

- [ElliCE: Efficient and Provably Robust Algorithmic Recourse via the Rashomon Sets](https://mlsanity.com/paper/2602.07674/) (2026)
- [Complete Evidence Extraction with Model Ensembles: A Case Study on Medical Coding](https://mlsanity.com/paper/2511.07055/) (2025)
- [EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs](https://mlsanity.com/paper/2512.22240/) (2025)
- [Analyzing the Temporal Factors for Anxiety and Depression Symptoms with the Rashomon Perspective](https://mlsanity.com/paper/2601.20874/) (2026)
- [Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity](https://mlsanity.com/paper/2602.00208/) (2026)
- [Equally Good, Yet Different: Benchmarking Rashomon sets in AutoML packages](https://mlsanity.com/paper/2609.36970/) (2026)

## Abstract (arXiv)

Automated machine learning systems efficiently streamline model selection but often focus on a single best-performing model, overlooking explanation uncertainty, an essential concern in human centered explainable AI. To address this, we propose a novel framework that incorporates model multiplicity into explanation generation by aggregating partial dependence profiles (PDP) from a set of near optimal models, known as the Rashomon set. The resulting Rashomon PDP captures interpretive variability and highlights areas of disagreement, providing users with a richer, uncertainty aware view of feature effects. To evaluate its usefulness, we introduce two quantitative metrics, the coverage rate and the mean width of confidence intervals, to evaluate the consistency between the standard PDP and the proposed Rashomon PDP. Experiments on 35 regression datasets from the OpenML CTR23 benchmark suite show that in most cases, the Rashomon PDP covers less than 70% of the best model's PDP, underscoring the limitations of single model explanations. Our findings suggest that Rashomon PDP improves the reliability and trustworthiness of model interpretations by adding additional information that would otherwise be neglected. This is particularly useful in high stakes domains where transparency and confidence are critical.