There are 33 repositories under explainable-ml topic.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome responsible machine learning resources.
Debugging, monitoring and visualization for Python Machine Learning and Data Science
đź”… Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
A collection of research papers and software related to explainability in graph machine learning.
Interpretability and explainability of data and machine learning models
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.
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Generate Diverse Counterfactual Explanations for any machine learning model.
moDel Agnostic Language for Exploration and eXplanation (JMLR 2018; JMLR 2021)
OmniXAI: A Library for eXplainable AI
đź’ Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
Examples of Data Science projects and Artificial Intelligence use-cases
H2O.ai Machine Learning Interpretability Resources
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
Neural network visualization toolkit for tf.keras
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
GraphXAI: Resource to support the development and evaluation of GNN explainers
Explaining the output of machine learning models with more accurately estimated Shapley values
A list of (post-hoc) XAI for time series
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
An Open-Source Library for the interpretability of time series classifiers
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
Fast approximate Shapley values in R
TalkToModel gives anyone with the powers of XAI through natural language conversations đź’¬!
Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
Explainable Machine Learning in Survival Analysis