# One Explanation to Rule them All -- Ensemble Consistent Explanations

arXiv: https://arxiv.org/abs/2205.08974 · HTML: https://mlsanity.com/paper/2205.08974/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 4 of 4)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- ["I know it when I see it". Visualization and Intuitive Interpretability](https://mlsanity.com/paper/1711.08042/) (2017)

## Built on by (most cited first, 7 of 7)

- [Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals](https://mlsanity.com/paper/2303.09297/) (2023)
- [The Effect of Data Poisoning on Counterfactual Explanations](https://mlsanity.com/paper/2402.08290/) (2024)
- [Reason to explain: Interactive contrastive explanations (REASONX)](https://mlsanity.com/paper/2305.18143/) (2023)
- [Adversarial Attacks on Leakage Detectors in Water Distribution Networks](https://mlsanity.com/paper/2306.06107/) (2023)
- [Fairness-Enhancing Ensemble Classification in Water Distribution Networks](https://mlsanity.com/paper/2410.13296/) (2024)
- [Interpretable Event Diagnosis in Water Distribution Networks](https://mlsanity.com/paper/2505.07299/) (2025)
- [The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations](https://mlsanity.com/paper/2602.00063/) (2026)

## Abstract (arXiv)

Transparency is a major requirement of modern AI based decision making systems deployed in real world. A popular approach for achieving transparency is by means of explanations. A wide variety of different explanations have been proposed for single decision making systems. In practice it is often the case to have a set (i.e. ensemble) of decisions that are used instead of a single decision only, in particular in complex systems. Unfortunately, explanation methods for single decision making systems are not easily applicable to ensembles -- i.e. they would yield an ensemble of individual explanations which are not necessarily consistent, hence less useful and more difficult to understand than a single consistent explanation of all observed phenomena. We propose a novel concept for consistently explaining an ensemble of decisions locally with a single explanation -- we introduce a formal concept, as well as a specific implementation using counterfactual explanations.