A Perspective on Explainable Artificial Intelligence Methods: SHAP and
LIME

A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

3 May 2023

Ahmed M. A. Salih

Z. Raisi-Estabragh

Steffen E. Petersen

Papers citing "A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME"

10 / 10 papers shown

Title
Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design Hansani Weeratunge Dominic Robe Elnaz Hajizadeh 36 0 0 10 May 2025
Exploring Convolutional Neural Networks for Rice Grain Classification: An Explainable AI Approach Muhammad Junaid Asif Hamza Khan Rabia Tehseen Syed Tahir Hussain Rizvi Mujtaba Asad Shazia Saqib Rana Fayyaz Ahmad 26 0 0 07 May 2025
Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids Wen Liu Tao Zhang Chenhui Lin K. Li Rui Wang 44 0 0 02 Mar 2025
Detecting Linguistic Indicators for Stereotype Assessment with Large Language Models Rebekka Görge Michael Mock Héctor Allende-Cid 75 0 0 26 Feb 2025
From Abstract to Actionable: Pairwise Shapley Values for Explainable AI Jiaxin Xu Hung Chau Angela Burden TDI 57 0 0 18 Feb 2025
Detecting anxiety and depression in dialogues: a multi-label and explainable approach Francisco de Arriba-Pérez Silvia García-Méndez AI4MH 41 0 0 23 Dec 2024
Learning Algorithms Made Simple Noorbakhsh Amiri Golilarz Elias Hossain Abdoljalil Addeh Keyan Alexander Rahimi AAML 57 0 0 11 Oct 2024
Improving Electrolyte Performance for Target Cathode Loading Using Interpretable Data-Driven Approach Vidushi Sharma Andy Tek Khanh Nguyen Max Giammona Murtaza Zohair Linda Sundberg Young-Hye La 33 3 0 03 Sep 2024
Are Linear Regression Models White Box and Interpretable? A. M. Salih Yuhe Wang XAI 29 2 0 16 Jul 2024
Selective Explanations Lucas Monteiro Paes Dennis L. Wei Flavio du Pin Calmon FAtt 40 0 0 29 May 2024