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ARTICLE
OPTIMIZING SHAP EXPLANATIONS: A COST-EFFECTIVE DATA SAMPLING METHOD FOR ENHANCED INTERPRETABILITY
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles
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Abstract
The proliferation of complex machine learning (ML) models in critical domains such as healthcare, finance, and real estate has underscored the urgent need for Explainable Artificial Intelligence (XAI) [2, 3, 4, 31, 43, 45]. While these "black-box" models often achieve superior predictive performance, their inherent lack of transparency hinders trust, accountability, and the ability to effectively debug and refine them. SHapley Additive exPlanations (SHAP) is a widely recognized model-agnostic XAI method that provides detailed insights into individual feature contributions to model predictions, robustly grounded in cooperative game theory [39, 58, 61]. However, a significant drawback of SHAP, particularly when applied to large datasets or computationally intensive models, is its substantial computational overhead, often rendering its application impractical in resource-constrained or real-time operational environments [60, 64]. This article proposes and rigorously investigates a data-efficient strategy for achieving SHAP interpretability by leveraging intelligent data reduction techniques. Specifically, we explore the application of Slovin's formula, a statistical sampling technique traditionally employed in survey research, as a low-cost heuristic for data reduction. Unlike more complex feature selection or dimensionality reduction methods, Slovin's formula requires minimal prior statistical knowledge of the dataset's properties, offering a straightforward, accessible, and efficient alternative for subsampling without extensive preprocessing. Through controlled experiments on synthetically generated datasets, we demonstrate that by judiciously sampling a representative subset of the original data, SHAP explanations can be generated with significantly reduced computational cost while maintaining a high degree of fidelity to the explanations derived from the full dataset. Our findings highlight a U-shaped trade-off in SHAP value stability: mid-ranked features tend to remain more stable under subsampling, whereas features with extreme (very low or very high) importance exhibit higher fluctuations. Furthermore, we observe that categorical and non-skewed distributed features generally maintain greater robustness, while highly skewed target distributions introduce increased variability. Crucially, the effectiveness and reliability of Slovin's formula diminish when the subsample-to-sample ratio falls below a critical threshold of approximately 5%. This empirical evaluation underscores the potential of our cost-effective approach to democratize access to advanced interpretability, enabling faster model insights, improved debugging, and broader, more sustainable deployment of transparent AI systems in various domains.
Keywords
References
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