# Predicting Satisfaction of Counterfactual Explanations from Human Ratings of Explanatory Qualities

arXiv: https://arxiv.org/abs/2504.13899 · HTML: https://mlsanity.com/paper/2504.13899/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Interpretable Counterfactual Explanations Guided by Prototypes](https://mlsanity.com/paper/1907.02584/) (2019)
- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [The Who in XAI: How AI Background Shapes Perceptions of AI Explanations](https://mlsanity.com/paper/2107.13509/) (2021)
- [CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations](https://mlsanity.com/paper/2108.08197/) (2021)
- [Evaluating Explanations Through LLMs: Beyond Traditional User Studies](https://mlsanity.com/paper/2410.17781/) (2024)
- [Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments](https://mlsanity.com/paper/2410.21131/) (2024)
- [Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence](https://mlsanity.com/paper/2404.12810/) (2024)
- [Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis](https://mlsanity.com/paper/2405.11958/) (2024)

## Built on by (most cited first, 2 of 2)

- [Do Metrics for Counterfactual Explanations Align with User Perception?](https://mlsanity.com/paper/2603.15607/) (2026)
- [Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations](https://mlsanity.com/paper/2610.16627/) (2026)

## Abstract (arXiv)

Counterfactual explanations are a widely used approach in Explainable AI, offering actionable insights into decision-making by illustrating how small changes to input data can lead to different outcomes. Despite their importance, evaluating the quality of counterfactual explanations remains an open problem. Traditional quantitative metrics, such as sparsity or proximity, fail to fully account for human preferences in explanations, while user studies are insightful but not scalable. Moreover, relying only on a single overall satisfaction rating does not lead to a nuanced understanding of why certain explanations are effective or not. To address this, we analyze a dataset of counterfactual explanations that were evaluated by 206 human participants, who rated not only overall satisfaction but also seven explanatory criteria: feasibility, coherence, complexity, understandability, completeness, fairness, and trust. Modeling overall satisfaction as a function of these criteria, we find that feasibility (the actionability of suggested changes) and trust (the belief that the changes would lead to the desired outcome) consistently stand out as the strongest predictors of user satisfaction, though completeness also emerges as a meaningful contributor. Crucially, even excluding feasibility and trust, other metrics explain 58% of the variance, highlighting the importance of additional explanatory qualities. Complexity appears independent, suggesting more detailed explanations do not necessarily reduce satisfaction. Strong metric correlations imply a latent structure in how users judge quality, and demographic background significantly shapes ranking patterns. These insights inform the design of counterfactual algorithms that adapt explanatory qualities to user expertise and domain context.