GLANCE: Global Actions in a Nutshell for Counterfactual Explainability

TL;DR

GLANCE is an adaptive algorithm for global counterfactual explanations that balances effectiveness, cost, and interpretability by considering data distribution and model structure.

Contribution

It introduces a novel agglomerative approach that jointly considers feature and action spaces for improved counterfactual explanations.

Findings

GLANCE outperforms existing methods in robustness and performance.

It effectively balances trade-offs among effectiveness, cost, and interpretability.

Experimental results show consistent improvements across datasets and models.

Abstract

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 $\texttt{GLANCE}$, 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 $\texttt{GLANCE}$ consistently shows greater robustness and performance compared to existing methods across various datasets and models.