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Top 23 explainability open source projects

removal-explanations
A lightweight implementation of removal-based explanations for ML models.
sage
For calculating global feature importance using Shapley values.
zennit
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
contextual-ai
Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.
responsible-ai-toolbox
This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
Transformer-MM-Explainability
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
spatio-temporal-brain
A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in rs-fMRI Data
ALPS 2021
XAI Tutorial for the Explainable AI track in the ALPS winter school 2021
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