📍 Interactive Studio for Explanatory Model Analysis

Boosting the AI research efficiency

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Thai Le, Suhang Wang, Dongwon Lee. 26th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining (KDD)

A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc

explainable

📺 A Python library for pruning and visualizing Keras Neural Networks' structure and weights

Explainable Machine Learning (Thessaloniki Machine Learning Meetup)

Trustworthy LoS Prediction Based on Multi-modal Data (AIME 2023)

Python framework for explainable omics analysis

A curated list of papers on explainability and interpretability of self-driving models

A 🐶🐱 explanation of generative neural nets

CT scan machine learning models including AxialNet and HiResCAM

Final report and implementation of my systems to help groups make decisions using arguments

This module extends the kernel SHAP method (as introduced by Lundberg and Lee (2017)) which is local in nature, to a method that computes global SHAP values.

A simple and explainable deep learning model for NLP.

Code for paper https://arxiv.org/abs/1910.04256

Structural Causal Model (SCM) approach to explainable reinforcement learning