CGL-LIME: A Robustified LIME with Copula-Based Sampling and Gradient-Weighted Kernels
The International Conference on Robust Statistics (ICORS), İstanbul, Türkiye, 20 - 24 Temmuz 2026, ss.1, (Özet Bildiri)
- Yayın Türü: Bildiri / Özet Bildiri
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.1
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
As increasingly complex and powerful AI models have emerged, the need to understand and explain their behavior has grown, giving rise to the field of eXplainable AI (XAI). In the existing AI literature, Local Interpretable Model-agnostic Explanations (LIME), one of the most widely used post-hoc explanation methods alongside SHAP, provides local interpretations by fitting a surrogate model to randomly perturbed samples. Despite its pioneering role in the development of local explanation techniques, LIME suffers from limited reproducibility. This limitation arises from the way neighborhood samples are generated, which can produce unrealistic observations. Consequently, the correlation between the variables may not be adequately preserved. This can reduce the fidelity of the surrogate model in the neighborhood of the observation being explained. In this study, we propose a robust extension of LIME that incorporates copula-based sampling and gradient-informed weighting. These modifications aim to generate more realistic samples and yield more robust local explanations. According to the well-established benchmark metrics, simulation studies demonstrate that the proposed method remains highly consistent with the standard LIME framework while providing improved performance.