Top 15 explainability Open-Source Projects

• shap

  38 21,580 9.4 Jupyter Notebook

  A game theoretic approach to explain the output of any machine learning model.

  

Project mention: Shap v0.45.0 | news.ycombinator.com | 2024-03-08

• awesome-production-machine-learning

  9 15,947 7.4

  A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

  

Project mention: Exploring Open-Source Alternatives to Landing AI for Robust MLOps | dev.to | 2023-12-13

One trove of treasures is the awesome-production-machine-learning repository on GitHub. This curated list provides a multitude of frameworks, libraries, and software designed to facilitate various stages of the ML lifecycle.

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• interpret

  6 5,988 9.7 C++

  Fit interpretable models. Explain blackbox machine learning.

  

• shapash

  8 2,642 8.6 Jupyter Notebook

  🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

  

Project mention: GitHub - MAIF/shapash: Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models | /r/learnmachinelearning | 2023-06-26

• Transformer-Explainability

  1 1,660 0.0 Jupyter Notebook

  [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

• responsible-ai-toolbox

  2 1,203 9.6 TypeScript

  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.

  

• Transformer-MM-Explainability

  3 701 0.0 Jupyter Notebook

  [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.

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• tf-keras-vis

  1 305 6.9 Python

  Neural network visualization toolkit for tf.keras

• CARLA

  2 263 0.0 Python

  CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms (by carla-recourse)

  

• whitebox

  12 181 2.8 Python

  [Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes (by squaredev-io)

  

• awesome-shapley-value

  1 127 3.2

  Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

  

• TalkToModel

  1 102 2.9 Python

  TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!

• sagemaker-explaining-credit-decisions

  2 94 2.4 Python

  Amazon SageMaker Solution for explaining credit decisions.

  

• augmented-interpretable-models

  1 37 7.4 Jupyter Notebook

  Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.

  

• CWOX

  1 8 5.9 Jupyter Notebook

  A XAI Framework to provide Contrastive Whole-output Explanation for Image Classification.

Project mention: Two-Stage Contrastive Whole Output Explaination (CWOX-2s) | news.ycombinator.com | 2023-06-13

• SaaSHub

  www.saashub.com sponsored

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

explainability related posts

• GitHub - MAIF/shapash: Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
  1 project | /r/learnmachinelearning | 26 Jun 2023
• How much of a full DS project pipeline can I do for free?
  1 project | /r/datascience | 18 Jul 2022
• Hacker News top posts: Jun 14, 2022
  2 projects | /r/hackerdigest | 14 Jun 2022
• Shapash – Python library to make machine learning interpretable
  1 project | news.ycombinator.com | 11 Jun 2022
• What Are the Most Important Statistical Ideas of the Past 50 Years?
  2 projects | news.ycombinator.com | 21 Feb 2022
• Deploying a LightGBM classifier as a AWS Sagemaker endpoint?
  2 projects | /r/mlops | 25 Aug 2021
• [P] It Is Now Possible To Generate a Model Audit Report with Shapash
  1 project | /r/MachineLearning | 31 May 2021
• A note from our sponsor - WorkOS
  workos.com | 24 Apr 2024

  The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning. Learn more →

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