imodels
imodels: a python package for fitting interpretable models - Published in JOSS (2021)
ExpFamilyPCA.jl
ExpFamilyPCA.jl: A Julia Package for Exponential Family Principal Component Analysis - Published in JOSS (2025)
quantus
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
corelay
CoRelAy is a tool to compose small-scale (single-machine) analysis pipelines.
transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
torchcam
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
virelay
ViRelAy is a visualization tool for the analysis of data as generated by CoRelAy.
multi-mode-cnn-pytorch
A PyTorch implementation of the Multi-Mode CNN to reconstruct Chlorophyll-a time series in the global ocean from oceanic and atmospheric physical drivers
asent
Asent is a python library for performing efficient and transparent sentiment analysis using spaCy.
hierarchical-dnn-interpretations
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
symbolic-governed-mistral-artifact
Tier-10 sealed governance artifact for Mistral-7B with exact-match benchmarks and symbolic verifier.
talktomodel
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
captum-tutorials
attention-meets-perturbation
📝 Official Implementation of "Attention Meets Perturbation: Robust and Interpretable Attention with Adversarial Training"
bcf-iv
Package for heterogeneous causal effects in the presence of imperfect compliance (e.g., instrumental variables, fuzzy regression discontinuity designs)
awesome-attention-heads
An awesome repository & A comprehensive survey on interpretability of LLM attention heads.
pnet_robustness
Reliable interpretability of biology-inspired deep neural networks
icml19-egocnn
Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)
awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
contrastiveexplanation
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University