Self-explanatory tutorials for different model-agnostic and model-specific XAI methods

counterfactuals: An R package for Counterfactual Explanation Methods

SLISEMAP: Combining supervised dimensionality reduction with local explanations

Local Universal Rule-based Explanations

GEMEX is a novel, model-agnostic Explainable AI (XAI) library grounded in Riemannian information geometry and differential geometry. It treats a trained model as defining a statistical manifold equipped with the Fisher Information Metric and derives all explanations from the intrinsic geometry of that manifold.

We've developed a powerful binary dog and cat image classifier, driven by advanced deep learning techniques, and enhanced its transparency using Local Interpretable Model-agnostic Explanations (LIME). Witness the magic as the model accurately predicts dog and cat images while LIME reveals the intricate decision-making process behind each result.

A machine learning project developing classification models to predict COVID-19 diagnosis in paediatric patients.

Classifier Analysis and Fairness Considerations

Analysis of hotel booking cancellations with EDA, preprocessing, basic model comparison, and model-agnostic interpretation.

LOMATCE: LOcal Model-Agnostic Time-series Classification Explanations

Code, models and data for our paper: K. Tsigos, E. Apostolidis, V. Mezaris, "An Integrated Framework for Multi-Granular Explanation of Video Summarization", Frontiers in Signal Processing, vol. 4, 2024

This repository presents a comprehensive research paper exploring the role of Explainable Artificial Intelligence (XAI) in modern Machine Learning. It aims to shed light on the interpretability of 'black-box' models like Neural Networks, Explainable AI and highlights the need for transparent, human-understandable ML systems.

Model-independent visual explanation methods for image classifiers.

Code, models and data for our paper: Th. Eleftheriadis, E. Apostolidis, V. Mezaris, "An Experimental Study on Generating Plausible Textual Explanations for Video Summarization", IEEE CBMI 2025, Special Session on Explainability in Multimedia Analysis (ExMA)