explainability
There are 420 repositories under explainability topic.
shap/shap
A game theoretic approach to explain the output of any machine learning model.
EthicalML/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
MAIF/shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
hila-chefer/Transformer-Explainability
[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
microsoft/responsible-ai-toolbox
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.
EthicalML/xai
XAI - An eXplainability toolbox for machine learning
CodeBoarding/CodeBoarding
🪄 Interactive Diagrams for Code
hila-chefer/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.
h1st-ai/h1st
Power Tools for AI Engineers With Deadlines
flyingdoog/awesome-graph-explainability-papers
Papers about explainability of GNNs
MisaOgura/flashtorch
Visualization toolkit for neural networks in PyTorch! Demo -->
mmschlk/shapiq
Shapley Interactions and Shapley Values for Machine Learning
alvinwan/neural-backed-decision-trees
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
xmed-lab/CLIP_Surgery
[Pattern Recognition 25] CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
explainX/explainx
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu
haofanwang/Score-CAM
Official implementation of Score-CAM in PyTorch
keisen/tf-keras-vis
Neural network visualization toolkit for tf.keras
hbaniecki/adversarial-explainable-ai
💡 Adversarial attacks on explanations and how to defend them
wisent-ai/wisent-guard
This is an open-source version of the representation engineering framework for stopping harmful outputs or hallucinations on the level of activations. 100% free, self-hosted and open-source.
carla-recourse/CARLA
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
iancovert/sage
For calculating global feature importance using Shapley values.
sebastian-hofstaetter/matchmaker
Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch
dmlc/GNNLens2
Visualization tool for Graph Neural Networks
AI4LIFE-GROUP/OpenXAI
OpenXAI : Towards a Transparent Evaluation of Model Explanations
chr5tphr/zennit
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
hate-alert/HateXplain
Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
mims-harvard/GraphXAI
GraphXAI: Resource to support the development and evaluation of GNN explainers
all-things-vits/code-samples
Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision.
feedzai/timeshap
TimeSHAP explains Recurrent Neural Network predictions.
charlesdedampierre/BunkaTopics
🗺️ Data Cleaning and Textual Data Visualization 🗺️
squaredev-io/whitebox
[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
csinva/imodelsX
Interpret text data with LLMs (sklearn compatible).
JHoelli/Awesome-Time-Series-Explainability
A list of (post-hoc) XAI for time series
pietrobarbiero/pytorch_explain
PyTorch Explain: Interpretable Deep Learning in Python.
marakeby/pnet_prostate_paper
P-NET, Biologically informed deep neural network for prostate cancer classification and discovery