Evaluation and Tracking for LLM Experiments

Real-time explainable machine learning for business optimisation

Comparisons of methods used to measure model interactions

Fit interpretable models. Explain blackbox machine learning.

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

A curated list of awesome responsible machine learning resources.

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

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

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

Interpretable Machine Learning via Rule Extraction

moDel Agnostic Language for Exploration and eXplanation

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.

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.

Rough set and machine learning data structures, algorithms and tools, including algorithms for discernibility matrix, reducts, decision rules, classification (RoughSet, KNN, RIONIDA, AQ15, C4.5, SVM, NeuralNetwork and many others), discretization (1R, Entropy Minimization, ChiMerge, MD), and tool for interactive and explainable machine learning.

Final year project, exploring the field of quantum machine learning.

👋 Xplique is a Neural Networks Explainability Toolbox