Fit interpretable models. Explain blackbox machine learning.

A Bachelor's Thesis project analyzing and comparing classifiers for breast cancer detection using fine needle aspiration biopsies. Includes Jupyter Notebooks for model training and evaluation, and a LaTeX document detailing the methodology and results. Features SHAP for explainable AI analysis.

Implementation of LIME focused on producing user-centric local explanations for image classifiers.

A curated list of awesome academic research, books, code of ethics, data sets, institutes, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible AI, Trustworthy AI, and Human-Centered AI.

Repository for benchmarking different post-hoc xai explanation methods on image datasets

Explain a black-box module in natural language.

A project focusing on binary classification using Explainable Artificial Intelligence (XAI) methods, specifically SHAP (SHapley Additive exPlanations), and Grid Search for hyperparameter tuning. The project utilizes EfficientNetV2-B0 architecture on the Cat VS Dog dataset.

SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

https://github.com/jphall663 -- https://github.com/jphall663/awesome-machine-learning-interpretability -- https://www.youtube.com/watch?v=d6j6bofhj2M&list=PLV8yxwGOxvvovp-j6ztxhF3QcKXT6vORU&index=3

Explainable Reinforcement Learning (XRL) Resources

Efficient R implementation of SHAP

Explain LLMs for Entity Resolution

GNN Explainability in a regression setting - semester project for Applied Mathematics MSc @ EPFL

Collecting fish image data, after training classifiers grad-cam is applied for the prediction interpretation

A curated list of awesome responsible machine learning resources.

Papers about explainability of GNNs

👋 Xplique is a Neural Networks Explainability Toolbox

An easier approach to using and understanding ML models

Prototypical Concept-based Explanations, accepted at SAIAD workshop at CVPR 2024.