LENS Project

Minimal starting point for rapid prototyping interactive Human-AI tools

Explain a black-box module in natural language.

Experiments with experimental rule-based models to go along with imodels.

XAI-Analytics is a tool that opens the black-box of machine learning. It helps the user to understand the decision-making process of machine learning models.

Code of experiments implemented in the paper "Explainability of Predictive Process Monitoring results: Techniques, Experiments and Lessons Learned", comparing XAI methods at different granularities (global/local) with different settings on predictive process monitoring outcomes using process mining event logs

A little lab of learning algorithms (in LUA). Less XAI, but better

In this article, the factors affecting BERT's transferability is explained through visualizations

A scoring system for explainability

Website for health data science at KDD 2021

Official Website of https://github.com/tamlhp/awesome-privex

HTML version of the book Feature Engineering & Selection for Explainable Models A Second Course for Data Scientists

Show case for modelStudio based on ⚽⚽⚽FIFA 20 ⚽⚽⚽

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

XAI를 활용한 서울 카페 리뷰 분석

This Tower of Hanoi Game Prototype is an attempt at explaining Q-learning and Reinforcement Learning Principles to People in an Intuitive and Interactive Way

Conway's Game of Life is sequential, here high-dimensional states are projected into the two-dimensional space, and connected, furthermore, meta-data is added to create interactive 2D visualizations.

Presentation given to Drexel XAI seminar on "Interpretable Explanations of Black Boxes by Meaningful Perturbation"

Showcase for Forecasting Explainability using New Car Sales in Norway

Explainable AI (XAI)