Python Explainability-requires-interactivity Resources

Responsible AI Workshop Responsible innovation is top of mind. As such, the tech industry as well as a growing number of organizations of all kinds in

TimeSHAP TimeSHAP is a model-agnostic, recurrent explainer that builds upon KernelSHAP and extends it to the sequential domain. TimeSHAP computes even

Interactivity Lab: Household Pulse Explorable Goal: Build an interactive application that incorporates fundamental Streamlit components to offer a cur

PressurePlate is a multi-agent environment that requires agents to cooperate during the traversal of a gridworld. The grid is partitioned into several rooms, and each room contains a plate and a closed doorway.

streamlit-shap This component provides a wrapper to display SHAP plots in Streamlit.

Interpretable Control Exploration and Counterfactual Explanation (ICE) on StyleGAN Which Style Makes Me Attractive? Interpretable Control Discovery an

py-12f-common About This repository holds those infrastructure-level modules, that every application requires that follows the core 12-factor principl

It's the assignment 1 from the Python 2 course, that requires a ToDoApp with authentication using Django

Awesome production machine learning This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, versi

Accompanying source code to the paper "Matrix Factorization for Collaborative Filtering is just Solving an Adjoint Latent Dirichlet Allocation Model A

fake-news-explainability Experiments for fake news explainability project This repository only contains the notebooks used to train the models and eva

🎉 What's new ? Version New Feature Description Tutorial 1.6.x Explainability Quality Metrics To help increase confidence in explainability methods, y

Good Semi-Supervised Learning that Requires a Bad GAN This is the code we used in our paper Good Semi-supervised Learning that Requires a Bad GAN Ziha

VSSTrigger Coerce authentication from Windows hosts via MS-FSRVP (Requires FS-VS

AT-BMC Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction (AAAI 2022) Paper Prerequisites Install pac

ReFine: Multi-Grained Explainability for GNNs This is the official code for Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 20

Explainable_FIQA_WITH_AMVA Note This is the official repository of the paper: Explainability of the Implications of Supervised and Unsupervised Face I

A community based economy bot with python works only with python 3.7.8 as web3 requires cytoolz has some issues building with python 3.10

Papers about explainability of GNNs

ReFine: Multi-Grained Explainability for GNNs We are trying hard to update the code, but it may take a while to complete due to our tight schedule rec

REMIX: Rule Extraction Methods for Interactive eXplainability This repository contains a variety of tools and methods for extracting interpretable rul

Rule-based Representation Learner This is a PyTorch implementation of Rule-based Representation Learner (RRL) as described in NeurIPS 2021 paper: Scal

XAI - An eXplainability toolbox for machine learning XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contai

InterpretML - Alpha Release In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be lig

napari-threedee A suite of useful tools based on 3D interactivity in napari This napari plugin was generated with Cookiecutter using @napari's cookiec

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.

Adaptive wavelets Wavelets which adapt given data (and optionally a pre-trained model). This yields models which are faster, more compressible, and mo

Can You Solve a Problem? A beginner friendly repository that requires you to solve familiar problems with python. This could be as simple as implement

Updated Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol. Introduction This balenaCloud (previously

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo

GNNLens2 is an interactive visualization tool for graph neural networks (GNN).

Blue noise image stippling in Processing (requires GPU)

👋🦊 Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.

Hierarchical neural-net interpretations (ACD) 🧠 Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Offic

Neural-Backed Decision Trees · Site · Paper · Blog · Video Alvin Wan, *Lisa Dunlap, *Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah

FlashTorch A Python visualization toolkit, built with PyTorch, for neural networks in PyTorch. Neural networks are often described as "black box". The

Werkzeug Debug Console Pin Bypass Werkzeug has a debug console that requires a pin by default. It's possible to bypass this with an LFI vulnerability

Explainability Requires Interactivity This repository contains the code to train all custom models used in the paper Explainability Requires Interacti

easyopt is a super simple yet super powerful optuna-based Hyperparameters Optimization Framework that requires no coding.

Thermostat is a large collection of NLP model explanations and accompanying analysis tools. Combines explainability methods from the captum library wi

CARLA - Counterfactual And Recourse Library CARLA is a python library to benchmark counterfactual explanation and recourse models. It comes out-of-the

A collection of research papers and software related to explainability in graph machine learning.

FlashTorch A Python visualization toolkit, built with PyTorch, for neural networks in PyTorch. Neural networks are often described as "black box". The

PyTorch Implementation of Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers 1 Using Colab Please notic

AI Explainability 360 (v0.2.1) The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datase

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo

Explainability for Vision Transformers (in PyTorch) This repository implements methods for explainability in Vision Transformers