Local Interpretable Model Agnostic Explanation

Local Interpretable Model-Agnostic Explanations (LIME) aim to make the predictions of complex "black box" machine learning models more understandable. Current research focuses on improving LIME's stability, fidelity (how well explanations reflect the model's behavior), and applicability to various data types (images, text, time series) and model architectures (including deep learning models like transformers and convolutional neural networks). This work is significant because it addresses the critical need for transparency and trust in AI systems, particularly in high-stakes domains like healthcare and finance, by providing more reliable and insightful explanations of model decisions.

Papers

December 10, 2022

Causality-Aware Local Interpretable Model-Agnostic Explanations
Martina Cinquini, Riccardo Guidotti
XAI Method Causal Relation Model Agnostic eXplainable Artificial Intelligence Causal Knowledge Local Interpretable Model Agnostic Explanation

November 2, 2022

Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach
Arnab Neelim Mazumder, Niall Lyons, Ashutosh Pandey, Avik Santra, Tinoosh Mohsenin
Loss Function Line by Line Explanation Real Power Model Explanation Local Interpretable Model Agnostic Explanation Neural Network Output Incremental Training LIME Explanation

October 7, 2022

Explainable AI based Glaucoma Detection using Transfer Learning and LIME
Touhidul Islam Chayan, Anita Islam, Eftykhar Rahman, Md. Tanzim Reza, Tasnim Sakib Apon, MD. Golam Rabiul Alam
Transfer Learning Explainable AI Glaucoma Diagnosis Local Interpretable Model Agnostic Explanation Retinal Fundus Glaucoma Challenge Dseg Lime

September 8, 2022

ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques
Junhao Liu, Xin Zhang
New Framework Line by Line Explanation Temporal Information Explanation Method Local Interpretable Model Agnostic Explanation

July 5, 2022

GLANCE: Global to Local Architecture-Neutral Concept-based Explanations
Avinash Kori, Ben Glocker, Francesca Toni
Glance Annotation Explainability Method Visual Explanation Local Interpretable Model Agnostic Explanation Interpretable Surrogate Model

May 16, 2022

Model Agnostic Local Explanations of Reject
André Artelt, Roel Visser, Barbara Hammer
Counterfactual Explanation Interpretable Model Local Interpretable Model Agnostic Explanation accuratE Prediction Risky Prompt Rejection Reject Option

April 7, 2022

Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME
Niloofar Ranjbar, Reza Safabakhsh
Inherent Interpretability Interpretable Model Local Interpretable Model Agnostic Explanation Local Interpretability Local Interpretable

March 4, 2022

Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths
Amir Hossein Akhavan Rahnama, Judith Butepage
LeArning Abstract Feature Importance Learning to Rank Local Interpretable Model Agnostic Explanation Explanation Quality Decision Path Input Ranking Model

February 15, 2022

LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data
Dilini Rajapaksha, Christoph Bergmeir
Application Proficiency State of the Art Forecasting Smart Meter Local Interpretable Model Agnostic Explanation Consumption Forecasting Global Forecasting Interpretable Forecast

February 2, 2022

Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners
Karthikeyan Natesan Ramamurthy, Amit Dhurandhar, Dennis Wei, Zaid Bin Tariq
Feature Attribution Post Hoc Explanation Similarity Learning Local Interpretable Model Agnostic Explanation Model Agnostic Explanation Creative Analogy

January 28, 2022

Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning
Amit Dhurandhar, Karthikeyan Ramamurthy, Kartik Ahuja, Vijay Arya
Interpretable Model Black Box Model Local Interpretable Model Agnostic Explanation Bidirectional Correspondence Quality Explanation

January 12, 2022

SLISEMAP: Supervised dimensionality reduction through local explanations
Anton Björklund, Jarmo Mäkelä, Kai Puolamäki
Black Box Model Local Explanation Local Interpretable Model Agnostic Explanation Global Explanation Supervised Dimensionality Reduction

January 4, 2022

McXai: Local model-agnostic explanation as two games
Yiran Huang, Nicole Schaal, Michael Hefenbrock, Yexu Zhou, Till Riedel, Likun Fang, Michael Beigl
Black Box Monte Carlo Tree Search Black Box Model Video Game Local Interpretable Model Agnostic Explanation Black Box Machine Learning Model Local Interpretability