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

Fit interpretable models. Explain blackbox machine learning.

Evaluation and Tracking for LLM Experiments

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

A curated list of awesome responsible machine learning resources.

👋 Xplique is a Neural Networks Explainability Toolbox

moDel Agnostic Language for Exploration and eXplanation

Interpretable Machine Learning via Rule Extraction

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Real-time explainable machine learning for business optimisation

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Comparisons of methods used to measure model interactions

Automating machine learning training and save an SQL version of the model

Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

AntakIA is THE tool to explain an ML model or replace it with a collection of basic explainable models.

Robust regression algorithm that can be used for explaining black box models (Python implementation)

A PyTorch implementation of constrained optimization and modeling techniques

Classification and Object Detection XAI methods (CAM-based, backpropagation-based, perturbation-based, statistic-based) for thyroid cancer ultrasound images

Fast and explainable clustering in Python

Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.