# GLANCE: Global Actions in a Nutshell for Counterfactual Explainability

arXiv: https://arxiv.org/abs/2405.18921 · HTML: https://mlsanity.com/paper/2405.18921/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 21 of 21)

- [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)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review](https://mlsanity.com/paper/2010.10596/) (2020)
- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [A survey of algorithmic recourse: definitions, formulations, solutions, and prospects](https://mlsanity.com/paper/2010.04050/) (2020)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models](https://mlsanity.com/paper/1905.07857/) (2019)
- [ExKMC: Expanding Explainable k-Means Clustering](https://mlsanity.com/paper/2006.02399/) (2020)
- [Global Counterfactual Explainer for Graph Neural Networks](https://mlsanity.com/paper/2210.11695/) (2022)
- [GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations](https://mlsanity.com/paper/2305.17021/) (2023)
- [On the price of explainability for some clustering problems](https://mlsanity.com/paper/2101.01576/) (2021)
- [Explainable k-means. Don't be greedy, plant bigger trees!](https://mlsanity.com/paper/2111.03193/) (2021)
- [Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms](https://mlsanity.com/paper/2103.01096/) (2021)
- [Global Counterfactual Explanations: Investigations, Implementations and Improvements](https://mlsanity.com/paper/2204.06917/) (2022)
- [Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals](https://mlsanity.com/paper/2303.09297/) (2023)
- [Fairness Aware Counterfactuals for Subgroups](https://mlsanity.com/paper/2306.14978/) (2023)
- [Generating collective counterfactual explanations in score-based classification via mathematical optimization](https://mlsanity.com/paper/2310.12822/) (2023)
- [HELOC Applicant Risk Performance Evaluation by Topological Hierarchical Decomposition](https://mlsanity.com/paper/1811.10658/) (2018)
- [Inverse Classification with Limited Budget and Maximum Number of Perturbed Samples](https://mlsanity.com/paper/2009.14111/) (2020)

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

- [Explaining Concept Drift through the Evolution of Group Counterfactuals](https://mlsanity.com/paper/2509.09616/) (2025)
- [Revealing Positive and Negative Role Models to Help People Make Good Decisions](https://mlsanity.com/paper/2603.02495/) (2026)
- [The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations](https://mlsanity.com/paper/2602.00063/) (2026)
- [GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering](https://mlsanity.com/paper/2603.05318/) (2026)
- [CEL: Comprehensive Counterfactual Explanations Library and Benchmark](https://mlsanity.com/paper/2607.22045/) (2026)
- [Challenges in Evaluating Explanation Methods for Static and Evolving Data](https://mlsanity.com/paper/2608.06351/) (2026)

## Abstract (arXiv)

The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expressed as actions to offer recourse, aim to provide succinct explanations and insights applicable to large population subgroups. High effectiveness, measured by the fraction of the population that is provided recourse, ensures that the actions benefit as many individuals as possible. Keeping the cost of actions low ensures the proposed recourse actions remain practical and actionable. Limiting the number of actions that provide global counterfactuals is essential to maximizing interpretability. The primary challenge, therefore, is to balance these trade-offs--maximizing effectiveness, minimizing cost, while maintaining a small number of actions. We introduce textttGLANCE, a versatile and adaptive algorithm that employs a novel agglomerative approach, jointly considering both the feature space and the space of counterfactual actions, thereby accounting for the distribution of points in a way that aligns with the model's structure. This design enables the careful balancing of the trade-offs among the three key objectives, with the size objective functioning as a tunable parameter to keep the actions few and easy to interpret. Our extensive experimental evaluation demonstrates that textttGLANCE consistently shows greater robustness and performance compared to existing methods across various datasets and models.